Intelligence system based classification approach for medical disease diagnosis

Science.gov (United States)

Sagir, Abdu Masanawa; Sathasivam, Saratha

2017-08-01

The prediction of breast cancer in women who have no signs or symptoms of the disease as well as survivability after undergone certain surgery has been a challenging problem for medical researchers. The decision about presence or absence of diseases depends on the physician's intuition, experience and skill for comparing current indicators with previous one than on knowledge rich data hidden in a database. This measure is a very crucial and challenging task. The goal is to predict patient condition by using an adaptive neuro fuzzy inference system (ANFIS) pre-processed by grid partitioning. To achieve an accurate diagnosis at this complex stage of symptom analysis, the physician may need efficient diagnosis system. A framework describes methodology for designing and evaluation of classification performances of two discrete ANFIS systems of hybrid learning algorithms least square estimates with Modified Levenberg-Marquardt and Gradient descent algorithms that can be used by physicians to accelerate diagnosis process. The proposed method's performance was evaluated based on training and test datasets with mammographic mass and Haberman's survival Datasets obtained from benchmarked datasets of University of California at Irvine's (UCI) machine learning repository. The robustness of the performance measuring total accuracy, sensitivity and specificity is examined. In comparison, the proposed method achieves superior performance when compared to conventional ANFIS based gradient descent algorithm and some related existing methods. The software used for the implementation is MATLAB R2014a (version 8.3) and executed in PC Intel Pentium IV E7400 processor with 2.80 GHz speed and 2.0 GB of RAM.

UIO-based Fault Diagnosis for Hydraulic Automatic Gauge Control System of Magnesium Sheet Mill

Directory of Open Access Journals (Sweden)

Li-Ping FAN

2014-02-01

Full Text Available Hydraulic automatic gauge control system of magnesium sheet mill is a complex integrated control system, which including mechanical, hydraulic and electrical comprehensive information. The failure rate of AGC system always is high, and its fault reasons are always complex. Based on analyzing the fault of main components of the automatic gauge control system, unknown input observer is used to realize fault diagnosis and isolation. Simulation results show that the fault diagnosis method based on the unknown input observer for the hydraulic automatic gauge control system of magnesium sheet mill is effective.

Model-Based Diagnosis and Prognosis of a Water Recycling System

Science.gov (United States)

Roychoudhury, Indranil; Hafiychuk, Vasyl; Goebel, Kai Frank

2013-01-01

A water recycling system (WRS) deployed at NASA Ames Research Center s Sustainability Base (an energy efficient office building that integrates some novel technologies developed for space applications) will serve as a testbed for long duration testing of next generation spacecraft water recycling systems for future human spaceflight missions. This system cleans graywater (waste water collected from sinks and showers) and recycles it into clean water. Like all engineered systems, the WRS is prone to standard degradation due to regular use, as well as other faults. Diagnostic and prognostic applications will be deployed on the WRS to ensure its safe, efficient, and correct operation. The diagnostic and prognostic results can be used to enable condition-based maintenance to avoid unplanned outages, and perhaps extend the useful life of the WRS. Diagnosis involves detecting when a fault occurs, isolating the root cause of the fault, and identifying the extent of damage. Prognosis involves predicting when the system will reach its end of life irrespective of whether an abnormal condition is present or not. In this paper, first, we develop a physics model of both nominal and faulty system behavior of the WRS. Then, we apply an integrated model-based diagnosis and prognosis framework to the simulation model of the WRS for several different fault scenarios to detect, isolate, and identify faults, and predict the end of life in each fault scenario, and present the experimental results.

A fault diagnosis and operation advising cooperative expert system based on multi-agent technology

Energy Technology Data Exchange (ETDEWEB)

Zhao, W.; Bai, X.; Ding, J.; Fang, Z.; Li, Z. [China Electric Power Research Inst., Haidian District, Beijing (China)

2006-07-01

Power systems are becoming more and more complex. In addition, the amount of real-time alarm messages from the supervisory control and data acquisition, energy management systems and wide area measurement systems about switchgear and protection are also increasing to a point far beyond the operator's capacity to digest the information. Research and development of a fault diagnosis system is necessary for the timely identification of fault or malfunctioning devices and for realizing the automation functions of dynamic supervisory control system. The prevailing fault diagnosis approaches in power systems include the expert system, artificial neural network, and fault diagnosis based on optimal theory. This paper discussed the advantages and disadvantages of each of these approaches for diagnosing faults. The paper also proposed a new fault diagnosis and operational processing approach based on a cooperative expert system combined with a multi-agent architecture. For solving complex and correlative faults, the cooperative expert system can overcome the deficiency of a single expert system. It can be used not only for diagnosing complex faults in real time but also in providing timely operational advice. The proposed system has been used successfully in a district power grid in China's Shangdong province for a year. 9 refs., 4 figs.

Development of a knowledge-based system for loop diagnosis

International Nuclear Information System (INIS)

Liao, L.Y.; Tang, H.C.; Chen, S.S.

1987-01-01

An accident diagnostic system is developed as an attempt to provide a useful aid for the operators of an experimental loop or a nuclear power plant in the case of emergency condition. Because the current practices in the system diagnosis are not satisfactory, there is an increasing demand on the establishment of various operator decision support systems. The knowledge based system is a new and promising technique which can be used to fulfill this demand. With the capability of automatic reasoning and by incorporating the information about system status, the knowledge based system can simulate the process of human thinking and serve as a good decision support system. This knowledge based decision support system can be helpful for both a fast, violent accident and a slowly developed accident. Specifically, a fast diagnostic report can be provided for a fast and violent accident of which time is the main concern and a complete diagnostic report can be provided for a slowly developed accident of which complexity is the main concern. Such a knowledge based decision support system also provides many other equally important advantages, such as the elimination of human error, the automatic validation of signal readings, the establishment of human error, the automatic validation of signal readings, and the establishment of a simulation environment

Knowledge-based fault diagnosis system for refuse collection vehicle

International Nuclear Information System (INIS)

Tan, CheeFai; Juffrizal, K.; Khalil, S. N.; Nidzamuddin, M. Y.

2015-01-01

The refuse collection vehicle is manufactured by local vehicle body manufacturer. Currently; the company supplied six model of the waste compactor truck to the local authority as well as waste management company. The company is facing difficulty to acquire the knowledge from the expert when the expert is absence. To solve the problem, the knowledge from the expert can be stored in the expert system. The expert system is able to provide necessary support to the company when the expert is not available. The implementation of the process and tool is able to be standardize and more accurate. The knowledge that input to the expert system is based on design guidelines and experience from the expert. This project highlighted another application on knowledge-based system (KBS) approached in trouble shooting of the refuse collection vehicle production process. The main aim of the research is to develop a novel expert fault diagnosis system framework for the refuse collection vehicle

Knowledge-based fault diagnosis system for refuse collection vehicle

Energy Technology Data Exchange (ETDEWEB)

Tan, CheeFai; Juffrizal, K.; Khalil, S. N.; Nidzamuddin, M. Y. [Centre of Advanced Research on Energy, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, Durian Tunggal, Melaka (Malaysia)

2015-05-15

The refuse collection vehicle is manufactured by local vehicle body manufacturer. Currently; the company supplied six model of the waste compactor truck to the local authority as well as waste management company. The company is facing difficulty to acquire the knowledge from the expert when the expert is absence. To solve the problem, the knowledge from the expert can be stored in the expert system. The expert system is able to provide necessary support to the company when the expert is not available. The implementation of the process and tool is able to be standardize and more accurate. The knowledge that input to the expert system is based on design guidelines and experience from the expert. This project highlighted another application on knowledge-based system (KBS) approached in trouble shooting of the refuse collection vehicle production process. The main aim of the research is to develop a novel expert fault diagnosis system framework for the refuse collection vehicle.

Direct costs of emergency medical care: a diagnosis-based case-mix classification system.

Science.gov (United States)

Baraff, L J; Cameron, J M; Sekhon, R

1991-01-01

To develop a diagnosis-based case mix classification system for emergency department patient visits based on direct costs of care designed for an outpatient setting. Prospective provider time study with collection of financial data from each hospital's accounts receivable system and medical information, including discharge diagnosis, from hospital medical records. Three community hospital EDs in Los Angeles County during selected times in 1984. Only direct costs of care were included: health care provider time, ED management and clerical personnel excluding registration, nonlabor ED expense including supplies, and ancillary hospital services. Indirect costs for hospitals and physicians, including depreciation and amortization, debt service, utilities, malpractice insurance, administration, billing, registration, and medical records were not included. Costs were derived by valuing provider time based on a formula using annual income or salary and fringe benefits, productivity and direct care factors, and using hospital direct cost to charge ratios. Physician costs were based on a national study of emergency physician income and excluded practice costs. Patients were classified into one of 216 emergency department groups (EDGs) on the basis of the discharge diagnosis, patient disposition, age, and the presence of a limited number of physician procedures. Total mean direct costs ranged from $23 for follow-up visit to $936 for trauma, admitted, with critical care procedure. The mean total direct costs for the 16,771 nonadmitted patients was $69. Of this, 34% was for ED costs, 45% was for ancillary service costs, and 21% was for physician costs. The mean total direct costs for the 1,955 admitted patients was $259. Of this, 23% was for ED costs, 63% was for ancillary service costs, and 14% was for physician costs. Laboratory and radiographic services accounted for approximately 85% of all ancillary service costs and 38% of total direct costs for nonadmitted patients

Computer-aided diagnosis workstation and network system for chest diagnosis based on multislice CT images

Science.gov (United States)

Satoh, Hitoshi; Niki, Noboru; Eguchi, Kenji; Moriyama, Noriyuki; Ohmatsu, Hironobu; Masuda, Hideo; Machida, Suguru

2008-03-01

Mass screening based on multi-helical CT images requires a considerable number of images to be read. It is this time-consuming step that makes the use of helical CT for mass screening impractical at present. To overcome this problem, we have provided diagnostic assistance methods to medical screening specialists by developing a lung cancer screening algorithm that automatically detects suspected lung cancers in helical CT images, a coronary artery calcification screening algorithm that automatically detects suspected coronary artery calcification and a vertebra body analysis algorithm for quantitative evaluation of osteoporosis likelihood by using helical CT scanner for the lung cancer mass screening. The function to observe suspicious shadow in detail are provided in computer-aided diagnosis workstation with these screening algorithms. We also have developed the telemedicine network by using Web medical image conference system with the security improvement of images transmission, Biometric fingerprint authentication system and Biometric face authentication system. Biometric face authentication used on site of telemedicine makes "Encryption of file" and Success in login" effective. As a result, patients' private information is protected. Based on these diagnostic assistance methods, we have developed a new computer-aided workstation and a new telemedicine network that can display suspected lesions three-dimensionally in a short time. The results of this study indicate that our radiological information system without film by using computer-aided diagnosis workstation and our telemedicine network system can increase diagnostic speed, diagnostic accuracy and security improvement of medical information.

Knowledge-based and integrated monitoring and diagnosis in autonomous power systems

Science.gov (United States)

Momoh, J. A.; Zhang, Z. Z.

1990-01-01

A new technique of knowledge-based and integrated monitoring and diagnosis (KBIMD) to deal with abnormalities and incipient or potential failures in autonomous power systems is presented. The KBIMD conception is discussed as a new function of autonomous power system automation. Available diagnostic modelling, system structure, principles and strategies are suggested. In order to verify the feasibility of the KBIMD, a preliminary prototype expert system is designed to simulate the KBIMD function in a main electric network of the autonomous power system.

Fault Diagnosis System of Wind Turbine Generator Based on Petri Net

Science.gov (United States)

Zhang, Han

Petri net is an important tool for discrete event dynamic systems modeling and analysis. And it has great ability to handle concurrent phenomena and non-deterministic phenomena. Currently Petri nets used in wind turbine fault diagnosis have not participated in the actual system. This article will combine the existing fuzzy Petri net algorithms; build wind turbine control system simulation based on Siemens S7-1200 PLC, while making matlab gui interface for migration of the system to different platforms.

Space nuclear reactor system diagnosis: Knowledge-based approach

International Nuclear Information System (INIS)

Ting, Y.T.D.

1990-01-01

SP-100 space nuclear reactor system development is a joint effort by the Department of Energy, the Department of Defense and the National Aeronautics and Space Administration. The system is designed to operate in isolation for many years, and is possibly subject to little or no remote maintenance. This dissertation proposes a knowledge based diagnostic system which, in principle, can diagnose the faults which can either cause reactor shutdown or lead to another serious problem. This framework in general can be applied to the fully specified system if detailed design information becomes available. The set of faults considered herein is identified based on heuristic knowledge about the system operation. The suitable approach to diagnostic problem solving is proposed after investigating the most prevalent methodologies in Artificial Intelligence as well as the causal analysis of the system. Deep causal knowledge modeling based on digraph, fault-tree or logic flowgraph methodology would present a need for some knowledge representation to handle the time dependent system behavior. A proposed qualitative temporal knowledge modeling methodology, using rules with specified time delay among the process variables, has been proposed and is used to develop the diagnostic sufficient rule set. The rule set has been modified by using a time zone approach to have a robust system design. The sufficient rule set is transformed to a sufficient and necessary one by searching the whole knowledge base. Qualitative data analysis is proposed in analyzing the measured data if in a real time situation. An expert system shell - Intelligence Compiler is used to develop the prototype system. Frames are used for the process variables. Forward chaining rules are used in monitoring and backward chaining rules are used in diagnosis

Remote diagnosis system for control and instrumentation systems

International Nuclear Information System (INIS)

Ito, Tetsuo; Suzuki, Satoshi; Nagaoka, Yukio.

1990-01-01

Control and instrumentation (C and I) systems for nuclear power plants tend to consist of many distributed digital controllers connected with transmission networks. Important parts of the C and I systems are redundantly constructed so that the failure of a component does not readily have a critical effect on the plant operation. It is necessary, however, to localize the faulty component for establishing better availability and maintainability of the plant. To diagnose failure of the C and I systems effectively, a remote diagnosis system is required that diagnoses anomalies of their controllers remotely from a central control room and identifies the fault location. Various fault diagnosis methods that apply artificial intelligence have been proposed for electronic circuits. Their knowledge bases are classified into two categories. One is rule-based knowledge, describing relations between anomaly phenomena and causes. The other is structure-based knowledge, which represents the configuration and functions of diagnosed objects. Though the latter is more suitable for deep inference, it is difficult to use for describing the detailed structure of large-scaled digital C and I systems. Then, a fault diagnosis system was developed that uses both knowledge bases and offers substantial man/machine interface functions for practical use

Development of diagnosis and maintenance support system for nuclear power plants with flexible inference function and knowledge base edition support function

International Nuclear Information System (INIS)

Fujii, Makoto; Seki, Eiji; Tai, Ichiro; Morioka, Toshihiko

1988-01-01

For the reliable and efficient diagnosis and inspection work of the nuclear power plant equipments, 'Diagnosis and Maintenance Support System' has been developed. This system has functions to assist operators or engineers to observe and evaluate equipment conditions based on the experts' knowledge. These functions are carried out through dialogue between the system and users. This system has two subsystems: diagnosis subsystem and knowledge base edition support subsystem. To achieve the functions of diagnosis subsystem, a new method of knowledge processing for equipment diagnosis is adopted. This method is based on the concept of 'Cause Generation and Checking'. Knowledge for diagnosis is represented with modularized production rules. And each rule module consists of four different type rules with hierarchical structure. With this approach, the system is equipped with sufficient performance not only in diagnosis function but also in flexible man-machine interface. Knowledge base edition support subsystem (Graphical Rule Editor) is provided for this system. This editor has functions to display and edit the contents of knowledge base with tree structures through the graphic display. With these functions, the efficiency of constructing expert system is highly improved. By applying this system to the maintenance support of neutron monitoring system, it is proved that this system has satisfactory performance as a diagnosis and maintenance support system. (author)

An expert fitness diagnosis system based on elastic cloud computing.

Science.gov (United States)

Tseng, Kevin C; Wu, Chia-Chuan

2014-01-01

This paper presents an expert diagnosis system based on cloud computing. It classifies a user's fitness level based on supervised machine learning techniques. This system is able to learn and make customized diagnoses according to the user's physiological data, such as age, gender, and body mass index (BMI). In addition, an elastic algorithm based on Poisson distribution is presented to allocate computation resources dynamically. It predicts the required resources in the future according to the exponential moving average of past observations. The experimental results show that Naïve Bayes is the best classifier with the highest accuracy (90.8%) and that the elastic algorithm is able to capture tightly the trend of requests generated from the Internet and thus assign corresponding computation resources to ensure the quality of service.

An Expert Fitness Diagnosis System Based on Elastic Cloud Computing

Directory of Open Access Journals (Sweden)

Kevin C. Tseng

2014-01-01

Full Text Available This paper presents an expert diagnosis system based on cloud computing. It classifies a user’s fitness level based on supervised machine learning techniques. This system is able to learn and make customized diagnoses according to the user’s physiological data, such as age, gender, and body mass index (BMI. In addition, an elastic algorithm based on Poisson distribution is presented to allocate computation resources dynamically. It predicts the required resources in the future according to the exponential moving average of past observations. The experimental results show that Naïve Bayes is the best classifier with the highest accuracy (90.8% and that the elastic algorithm is able to capture tightly the trend of requests generated from the Internet and thus assign corresponding computation resources to ensure the quality of service.

Fault Diagnosis for Hydraulic Servo System Using Compressed Random Subspace Based ReliefF

Directory of Open Access Journals (Sweden)

Yu Ding

2018-01-01

Full Text Available Playing an important role in electromechanical systems, hydraulic servo system is crucial to mechanical systems like engineering machinery, metallurgical machinery, ships, and other equipment. Fault diagnosis based on monitoring and sensory signals plays an important role in avoiding catastrophic accidents and enormous economic losses. This study presents a fault diagnosis scheme for hydraulic servo system using compressed random subspace based ReliefF (CRSR method. From the point of view of feature selection, the scheme utilizes CRSR method to determine the most stable feature combination that contains the most adequate information simultaneously. Based on the feature selection structure of ReliefF, CRSR employs feature integration rules in the compressed domain. Meanwhile, CRSR substitutes information entropy and fuzzy membership for traditional distance measurement index. The proposed CRSR method is able to enhance the robustness of the feature information against interference while selecting the feature combination with balanced information expressing ability. To demonstrate the effectiveness of the proposed CRSR method, a hydraulic servo system joint simulation model is constructed by HyPneu and Simulink, and three fault modes are injected to generate the validation data.

A Multi-Model Approach for System Diagnosis

DEFF Research Database (Denmark)

Niemann, Hans Henrik; Poulsen, Niels Kjølstad; Bækgaard, Mikkel Ask Buur

2007-01-01

A multi-model approach for system diagnosis is presented in this paper. The relation with fault diagnosis as well as performance validation is considered. The approach is based on testing a number of pre-described models and find which one is the best. It is based on an active approach......,i.e. an auxiliary input to the system is applied. The multi-model approach is applied on a wind turbine system....

Accident diagnosis system based on real-time decision tree expert system

Science.gov (United States)

Nicolau, Andressa dos S.; Augusto, João P. da S. C.; Schirru, Roberto

2017-06-01

Safety is one of the most studied topics when referring to power stations. For that reason, sensors and alarms develop an important role in environmental and human protection. When abnormal event happens, it triggers a chain of alarms that must be, somehow, checked by the control room operators. In this case, diagnosis support system can help operators to accurately identify the possible root-cause of the problem in short time. In this article, we present a computational model of a generic diagnose support system based on artificial intelligence, that was applied on the dataset of two real power stations: Angra1 Nuclear Power Plant and Santo Antônio Hydroelectric Plant. The proposed system processes all the information logged in the sequence of events before a shutdown signal using the expert's knowledge inputted into an expert system indicating the chain of events, from the shutdown signal to its root-cause. The results of both applications showed that the support system is a potential tool to help the control room operators identify abnormal events, as accidents and consequently increase the safety.

Matrix Failure Modes and Effects Analysis as a Knowledge Base for a Real Time Automated Diagnosis Expert System

Science.gov (United States)

Herrin, Stephanie; Iverson, David; Spukovska, Lilly; Souza, Kenneth A. (Technical Monitor)

1994-01-01

Failure Modes and Effects Analysis contain a wealth of information that can be used to create the knowledge base required for building automated diagnostic Expert systems. A real time monitoring and diagnosis expert system based on an actual NASA project's matrix failure modes and effects analysis was developed. This Expert system Was developed at NASA Ames Research Center. This system was first used as a case study to monitor the Research Animal Holding Facility (RAHF), a Space Shuttle payload that is used to house and monitor animals in orbit so the effects of space flight and microgravity can be studied. The techniques developed for the RAHF monitoring and diagnosis Expert system are general enough to be used for monitoring and diagnosis of a variety of other systems that undergo a Matrix FMEA. This automated diagnosis system was successfully used on-line and validated on the Space Shuttle flight STS-58, mission SLS-2 in October 1993.

Chaos Synchronization Based Novel Real-Time Intelligent Fault Diagnosis for Photovoltaic Systems

Directory of Open Access Journals (Sweden)

Chin-Tsung Hsieh

2014-01-01

Full Text Available The traditional solar photovoltaic fault diagnosis system needs two to three sets of sensing elements to capture fault signals as fault features and many fault diagnosis methods cannot be applied with real time. The fault diagnosis method proposed in this study needs only one set of sensing elements to intercept the fault features of the system, which can be real-time-diagnosed by creating the fault data of only one set of sensors. The aforesaid two points reduce the cost and fault diagnosis time. It can improve the construction of the huge database. This study used Matlab to simulate the faults in the solar photovoltaic system. The maximum power point tracker (MPPT is used to keep a stable power supply to the system when the system has faults. The characteristic signal of system fault voltage is captured and recorded, and the dynamic error of the fault voltage signal is extracted by chaos synchronization. Then, the extension engineering is used to implement the fault diagnosis. Finally, the overall fault diagnosis system only needs to capture the voltage signal of the solar photovoltaic system, and the fault type can be diagnosed instantly.

A systems biology-based classifier for hepatocellular carcinoma diagnosis.

Directory of Open Access Journals (Sweden)

Yanqiong Zhang

Full Text Available AIM: The diagnosis of hepatocellular carcinoma (HCC in the early stage is crucial to the application of curative treatments which are the only hope for increasing the life expectancy of patients. Recently, several large-scale studies have shed light on this problem through analysis of gene expression profiles to identify markers correlated with HCC progression. However, those marker sets shared few genes in common and were poorly validated using independent data. Therefore, we developed a systems biology based classifier by combining the differential gene expression with topological features of human protein interaction networks to enhance the ability of HCC diagnosis. METHODS AND RESULTS: In the Oncomine platform, genes differentially expressed in HCC tissues relative to their corresponding normal tissues were filtered by a corrected Q value cut-off and Concept filters. The identified genes that are common to different microarray datasets were chosen as the candidate markers. Then, their networks were analyzed by GeneGO Meta-Core software and the hub genes were chosen. After that, an HCC diagnostic classifier was constructed by Partial Least Squares modeling based on the microarray gene expression data of the hub genes. Validations of diagnostic performance showed that this classifier had high predictive accuracy (85.88∼92.71% and area under ROC curve (approximating 1.0, and that the network topological features integrated into this classifier contribute greatly to improving the predictive performance. Furthermore, it has been demonstrated that this modeling strategy is not only applicable to HCC, but also to other cancers. CONCLUSION: Our analysis suggests that the systems biology-based classifier that combines the differential gene expression and topological features of human protein interaction network may enhance the diagnostic performance of HCC classifier.

Fuzzy fault diagnosis system of MCFC

Institute of Scientific and Technical Information of China (English)

Wang Zhenlei; Qian Feng; Cao Guangyi

2005-01-01

A kind of fault diagnosis system of molten carbonate fuel cell (MCFC) stack is proposed in this paper. It is composed of a fuzzy neural network (FNN) and a fault diagnosis element. FNN is able to deal with the information of the expert knowledge and the experiment data efficiently. It also has the ability to approximate any smooth system. FNN is used to identify the fault diagnosis model of MCFC stack. The fuzzy fault decision element can diagnose the state of the MCFC generating system, normal or fault, and can decide the type of the fault based on the outputs of FNN model and the MCFC system. Some simulation experiment results are demonstrated in this paper.

The PCR-Based Diagnosis of Central Nervous System Tuberculosis: Up to Date

Directory of Open Access Journals (Sweden)

Teruyuki Takahashi

2012-01-01

Full Text Available Central nervous system (CNS tuberculosis, particularly tuberculous meningitis (TBM, is the severest form of Mycobacterium tuberculosis (M.Tb infection, causing death or severe neurological defects in more than half of those affected, in spite of recent advancements in available anti-tuberculosis treatment. The definitive diagnosis of CNS tuberculosis depends upon the detection of M.Tb bacilli in the cerebrospinal fluid (CSF. At present, the diagnosis of CNS tuberculosis remains a complex issue because the most widely used conventional “gold standard” based on bacteriological detection methods, such as direct smear and culture identification, cannot rapidly detect M.Tb in CSF specimens with sufficient sensitivity in the acute phase of TBM. Recently, instead of the conventional “gold standard”, the various molecular-based methods including nucleic acid amplification (NAA assay technique, particularly polymerase chain reaction (PCR assay, has emerged as a promising new method for the diagnosis of CNS tuberculosis because of its rapidity, sensitivity and specificity. In addition, the innovation of nested PCR assay technique is worthy of note given its contribution to improve the diagnosis of CNS tuberculosis. In this review, an overview of recent progress of the NAA methods, mainly highlighting the PCR assay technique, was presented.

Computer-aided diagnosis workstation and telemedicine network system for chest diagnosis based on multislice CT images

Science.gov (United States)

Satoh, Hitoshi; Niki, Noboru; Eguchi, Kenji; Ohmatsu, Hironobu; Kakinuma, Ryutaru; Moriyama, Noriyuki

2009-02-01

Mass screening based on multi-helical CT images requires a considerable number of images to be read. It is this time-consuming step that makes the use of helical CT for mass screening impractical at present. Moreover, the doctor who diagnoses a medical image is insufficient in Japan. To overcome these problems, we have provided diagnostic assistance methods to medical screening specialists by developing a lung cancer screening algorithm that automatically detects suspected lung cancers in helical CT images, a coronary artery calcification screening algorithm that automatically detects suspected coronary artery calcification and a vertebra body analysis algorithm for quantitative evaluation of osteoporosis likelihood by using helical CT scanner for the lung cancer mass screening. The functions to observe suspicious shadow in detail are provided in computer-aided diagnosis workstation with these screening algorithms. We also have developed the telemedicine network by using Web medical image conference system with the security improvement of images transmission, Biometric fingerprint authentication system and Biometric face authentication system. Biometric face authentication used on site of telemedicine makes "Encryption of file" and "Success in login" effective. As a result, patients' private information is protected. We can share the screen of Web medical image conference system from two or more web conference terminals at the same time. An opinion can be exchanged mutually by using a camera and a microphone that are connected with workstation. Based on these diagnostic assistance methods, we have developed a new computer-aided workstation and a new telemedicine network that can display suspected lesions three-dimensionally in a short time. The results of this study indicate that our radiological information system without film by using computer-aided diagnosis workstation and our telemedicine network system can increase diagnostic speed, diagnostic accuracy and

A Novel Mittag-Leffler Kernel Based Hybrid Fault Diagnosis Method for Wheeled Robot Driving System

Directory of Open Access Journals (Sweden)

Xianfeng Yuan

2015-01-01

presents a novel hybrid fault diagnosis framework based on Mittag-Leffler kernel (ML-kernel support vector machine (SVM and Dempster-Shafer (D-S fusion. Using sensor data sampled under different running conditions, the proposed approach initially establishes multiple principal component analysis (PCA models for fault feature extraction. The fault feature vectors are then applied to train the probabilistic SVM (PSVM classifiers that arrive at a preliminary fault diagnosis. To improve the accuracy of preliminary results, a novel ML-kernel based PSVM classifier is proposed in this paper, and the positive definiteness of the ML-kernel is proved as well. The basic probability assignments (BPAs are defined based on the preliminary fault diagnosis results and their confidence values. Eventually, the final fault diagnosis result is archived by the fusion of the BPAs. Experimental results show that the proposed framework not only is capable of detecting and identifying the faults in the robot driving system, but also has better performance in stability and diagnosis accuracy compared with the traditional methods.

Expert system for nuclear power plant feedwater system diagnosis

International Nuclear Information System (INIS)

Meguro, R.; Kinoshita, Y.; Sato, T.; Yokota, Y.; Yokota, M.

1987-01-01

The Expert System for Nuclear Power Plant Feedwater System Diagnosis has been developed to assist maintenance engineers in nuclear power plants. This system adopts the latest process computer TOSBAC G8050 and the expert system developing tool TDES2, and has a large scale knowledge base which consists of the expert knowledge and experience of engineers in many fields. The man-machine system, which has been developed exclusively for diagnosis, improves the man-machine interface and realizes the graphic displays of diagnostic process and path, stores diagnostic results and searches past reference

Computer-Based Image Analysis for Plus Disease Diagnosis in Retinopathy of Prematurity: Performance of the "i-ROP" System and Image Features Associated With Expert Diagnosis.

Science.gov (United States)

Ataer-Cansizoglu, Esra; Bolon-Canedo, Veronica; Campbell, J Peter; Bozkurt, Alican; Erdogmus, Deniz; Kalpathy-Cramer, Jayashree; Patel, Samir; Jonas, Karyn; Chan, R V Paul; Ostmo, Susan; Chiang, Michael F

2015-11-01

We developed and evaluated the performance of a novel computer-based image analysis system for grading plus disease in retinopathy of prematurity (ROP), and identified the image features, shapes, and sizes that best correlate with expert diagnosis. A dataset of 77 wide-angle retinal images from infants screened for ROP was collected. A reference standard diagnosis was determined for each image by combining image grading from 3 experts with the clinical diagnosis from ophthalmoscopic examination. Manually segmented images were cropped into a range of shapes and sizes, and a computer algorithm was developed to extract tortuosity and dilation features from arteries and veins. Each feature was fed into our system to identify the set of characteristics that yielded the highest-performing system compared to the reference standard, which we refer to as the "i-ROP" system. Among the tested crop shapes, sizes, and measured features, point-based measurements of arterial and venous tortuosity (combined), and a large circular cropped image (with radius 6 times the disc diameter), provided the highest diagnostic accuracy. The i-ROP system achieved 95% accuracy for classifying preplus and plus disease compared to the reference standard. This was comparable to the performance of the 3 individual experts (96%, 94%, 92%), and significantly higher than the mean performance of 31 nonexperts (81%). This comprehensive analysis of computer-based plus disease suggests that it may be feasible to develop a fully-automated system based on wide-angle retinal images that performs comparably to expert graders at three-level plus disease discrimination. Computer-based image analysis, using objective and quantitative retinal vascular features, has potential to complement clinical ROP diagnosis by ophthalmologists.

The fault monitoring and diagnosis knowledge-based system for space power systems: AMPERES, phase 1

Science.gov (United States)

Lee, S. C.

1989-01-01

The objective is to develop a real time fault monitoring and diagnosis knowledge-based system (KBS) for space power systems which can save costly operational manpower and can achieve more reliable space power system operation. The proposed KBS was developed using the Autonomously Managed Power System (AMPS) test facility currently installed at NASA Marshall Space Flight Center (MSFC), but the basic approach taken for this project could be applicable for other space power systems. The proposed KBS is entitled Autonomously Managed Power-System Extendible Real-time Expert System (AMPERES). In Phase 1 the emphasis was put on the design of the overall KBS, the identification of the basic research required, the initial performance of the research, and the development of a prototype KBS. In Phase 2, emphasis is put on the completion of the research initiated in Phase 1, and the enhancement of the prototype KBS developed in Phase 1. This enhancement is intended to achieve a working real time KBS incorporated with the NASA space power system test facilities. Three major research areas were identified and progress was made in each area. These areas are real time data acquisition and its supporting data structure; sensor value validations; development of inference scheme for effective fault monitoring and diagnosis, and its supporting knowledge representation scheme.

A fault diagnosis system for PV power station based on global partitioned gradually approximation method

Science.gov (United States)

Wang, S.; Zhang, X. N.; Gao, D. D.; Liu, H. X.; Ye, J.; Li, L. R.

2016-08-01

As the solar photovoltaic (PV) power is applied extensively, more attentions are paid to the maintenance and fault diagnosis of PV power plants. Based on analysis of the structure of PV power station, the global partitioned gradually approximation method is proposed as a fault diagnosis algorithm to determine and locate the fault of PV panels. The PV array is divided into 16x16 blocks and numbered. On the basis of modularly processing of the PV array, the current values of each block are analyzed. The mean current value of each block is used for calculating the fault weigh factor. The fault threshold is defined to determine the fault, and the shade is considered to reduce the probability of misjudgments. A fault diagnosis system is designed and implemented with LabVIEW. And it has some functions including the data realtime display, online check, statistics, real-time prediction and fault diagnosis. Through the data from PV plants, the algorithm is verified. The results show that the fault diagnosis results are accurate, and the system works well. The validity and the possibility of the system are verified by the results as well. The developed system will be benefit for the maintenance and management of large scale PV array.

An operator support system for research reactor operations and fault diagnosis through a connectionist framework and PSA based knowledge based systems

International Nuclear Information System (INIS)

Varde, P.V.; Sankar, S.; Verma, A.K.

1998-01-01

During reactor upset/abnormal conditions, emphasis is placed on the plant operator's ability to quickly identify the problem and perform diagnosis and initiate recovery action to ensure the safety of the plant. However, the reliability of human action is adversely affected at the time of crisis due to time stress and psychological factors. The availability of operational aids capable of monitoring the status of the plant and quickly identifying the deviation from normal operation is expected to significantly improve the operator reliability. The development of operator support systems using probabilistic safety assessment (PSA) techniques and information is finding wide application in nuclear plant operation. Often it is observed that most of the applications use a rule-based approach for diagnosis as well as safety status/transient conditions monitoring. A more efficient approach using artificial neural networks for safety status/transient condition monitoring and rule-based systems for diagnosis and emergency procedure generation has been applied for the development of a prototype operator adviser (OPAD) system for a 100 MW(th) heavy water moderated, cooled and natural uranium fueled research reactor. The development objective of this system is to improve the reliability of operator action and hence the reactor safety at the time of crisis as well as in normal operation. In order to address safety objectives at various stages of development of OPAD, the PSA techniques and tools have been used for knowledge representation. It has been demonstrated, with recall tests on the artificial neural network, that it can efficiently identify the reactor status in real-time scenario. This paper discusses various issues related to the development of an operator support system in a comprehensive way, right from the study of safety objectives, to data collection, to implementation of such a system

Entropy-Based Voltage Fault Diagnosis of Battery Systems for Electric Vehicles

Directory of Open Access Journals (Sweden)

Peng Liu

2018-01-01

Full Text Available The battery is a key component and the major fault source in electric vehicles (EVs. Ensuring power battery safety is of great significance to make the diagnosis more effective and predict the occurrence of faults, for the power battery is one of the core technologies of EVs. This paper proposes a voltage fault diagnosis detection mechanism using entropy theory which is demonstrated in an EV with a multiple-cell battery system during an actual operation situation. The preliminary analysis, after collecting and preprocessing the typical data periods from Operation Service and Management Center for Electric Vehicle (OSMC-EV in Beijing, shows that overvoltage fault for Li-ion batteries cell can be observed from the voltage curves. To further locate abnormal cells and predict faults, an entropy weight method is established to calculate the objective weight, which reduces the subjectivity and improves the reliability. The result clearly identifies the abnormity of cell voltage. The proposed diagnostic model can be used for EV real-time diagnosis without laboratory testing methods. It is more effective than traditional methods based on contrastive analysis.

Performance based fault diagnosis

DEFF Research Database (Denmark)

Niemann, Hans Henrik

2002-01-01

Different aspects of fault detection and fault isolation in closed-loop systems are considered. It is shown that using the standard setup known from feedback control, it is possible to formulate fault diagnosis problems based on a performance index in this general standard setup. It is also shown...

Fault Detection and Diagnosis System in Process industry Based on Big Data and WeChat

Directory of Open Access Journals (Sweden)

Sun Zengqiang

2017-01-01

Full Text Available The fault detection and diagnosis information in process industry can be received, anytime and anywhere, based on bigdata and WeChat with mobile phone, which got rid of constraints that can only check Distributed Control System (DCS in the central control room or look over in office. Then, fault detection, diagnosis information sharing can be provided, and what’s more, fault detection alarm range, code and inform time can be personalized. The pressure of managers who worked on process industry can be release with the mobile information system.

Active fault diagnosis in closed-loop uncertain systems

DEFF Research Database (Denmark)

Niemann, Hans Henrik

2006-01-01

Fault diagnosis of parametric faults in closed-loop uncertain systems by using an auxiliary input vector is considered in this paper, i.e. active fault diagnosis (AFD). The active fault diagnosis is based directly on the socalled fault signature matrix, related to the YJBK (Youla, Jabr, Bongiorno...... and Kucera) parameterization. Conditions are given for exact detection and isolation of parametric faults in closed-loop uncertain systems....

Computer-aided diagnosis workstation and data base system for chest diagnosis based on multihelical CT images

International Nuclear Information System (INIS)

Satoh, H.; Niki, N.; Eguchi, K.; Masuda, H.; Machida, S.; Moriyama, N.

2006-01-01

We have provided diagnostic assistance methods to medical screening specialists by developing a lung cancer screening algorithm that automatically detects suspected lung cancers in helical CT images and a coronary artery calcification screening algorithm that automatically detects suspected coronary artery calcification. We also have developed electronic medical recording system and prototype internet system for the community health in two or more regions by using the Virtual Private Network router, Biometric fingerprint authentication system and Biometric face authentication system for safety of medical information. The results of this study indicate that our computer-aided diagnosis workstation and network system can increase diagnostic speed, diagnostic accuracy and safety of medical information. (author)

An expert system in medical diagnosis

International Nuclear Information System (INIS)

Raboanary, R.; Raoelina Andriambololona; Soffer, J.; Raboanary, J.

2001-01-01

Health problem is still a crucial one in some countries. It is so important that it becomes a major handicap in economic and social development. In order to solve this problem, we have conceived an expert system that we called MITSABO, which means TO HEAL, to help the physicians to diagnose tropical diseases. It is clear that by extending the data base and the knowledge base, we can extend the application of the software to more general areas. In our expert system, we used the concept of 'self organization' of neural network based on the determination of the eigenvalues and the eigenvectors associated to the correlation matrix XX t . The projection of the data on the two first eigenvectors gives a classification of the diseases which is used to get a first approach in the diagnosis of the patient. This diagnosis is improved by using an expert system which is built from the knowledge base.

An Expert System for Diagnosis of Sleep Disorder Using Fuzzy Rule-Based Classification Systems

Science.gov (United States)

Septem Riza, Lala; Pradini, Mila; Fitrajaya Rahman, Eka; Rasim

2017-03-01

Sleep disorder is an anomaly that could cause problems for someone’ sleeping pattern. Nowadays, it becomes an issue since people are getting busy with their own business and have no time to visit the doctors. Therefore, this research aims to develop a system used for diagnosis of sleep disorder using Fuzzy Rule-Based Classification System (FRBCS). FRBCS is a method based on the fuzzy set concepts. It consists of two steps: (i) constructing a model/knowledge involving rulebase and database, and (ii) prediction over new data. In this case, the knowledge is obtained from experts whereas in the prediction stage, we perform fuzzification, inference, and classification. Then, a platform implementing the method is built with a combination between PHP and the R programming language using the “Shiny” package. To validate the system that has been made, some experiments have been done using data from a psychiatric hospital in West Java, Indonesia. Accuracy of the result and computation time are 84.85% and 0.0133 seconds, respectively.

Fault tolerant control based on active fault diagnosis

DEFF Research Database (Denmark)

Niemann, Hans Henrik

2005-01-01

An active fault diagnosis (AFD) method will be considered in this paper in connection with a Fault Tolerant Control (FTC) architecture based on the YJBK parameterization of all stabilizing controllers. The architecture consists of a fault diagnosis (FD) part and a controller reconfiguration (CR......) part. The FTC architecture can be applied for additive faults, parametric faults, and for system structural changes. Only parametric faults will be considered in this paper. The main focus in this paper is on the use of the new approach of active fault diagnosis in connection with FTC. The active fault...... diagnosis approach is based on including an auxiliary input in the system. A fault signature matrix is introduced in connection with AFD, given as the transfer function from the auxiliary input to the residual output. This can be considered as a generalization of the passive fault diagnosis case, where...

Approximation Algorithms for Model-Based Diagnosis

NARCIS (Netherlands)

Feldman, A.B.

2010-01-01

Model-based diagnosis is an area of abductive inference that uses a system model, together with observations about system behavior, to isolate sets of faulty components (diagnoses) that explain the observed behavior, according to some minimality criterion. This thesis presents greedy approximation

Computer-aided diagnosis workstation and database system for chest diagnosis based on multi-helical CT images

Science.gov (United States)

Satoh, Hitoshi; Niki, Noboru; Mori, Kiyoshi; Eguchi, Kenji; Kaneko, Masahiro; Kakinuma, Ryutarou; Moriyama, Noriyuki; Ohmatsu, Hironobu; Masuda, Hideo; Machida, Suguru; Sasagawa, Michizou

2006-03-01

Multi-helical CT scanner advanced remarkably at the speed at which the chest CT images were acquired for mass screening. Mass screening based on multi-helical CT images requires a considerable number of images to be read. It is this time-consuming step that makes the use of helical CT for mass screening impractical at present. To overcome this problem, we have provided diagnostic assistance methods to medical screening specialists by developing a lung cancer screening algorithm that automatically detects suspected lung cancers in helical CT images and a coronary artery calcification screening algorithm that automatically detects suspected coronary artery calcification. We also have developed electronic medical recording system and prototype internet system for the community health in two or more regions by using the Virtual Private Network router and Biometric fingerprint authentication system and Biometric face authentication system for safety of medical information. Based on these diagnostic assistance methods, we have now developed a new computer-aided workstation and database that can display suspected lesions three-dimensionally in a short time. This paper describes basic studies that have been conducted to evaluate this new system. The results of this study indicate that our computer-aided diagnosis workstation and network system can increase diagnostic speed, diagnostic accuracy and safety of medical information.

Detector design for active fault diagnosis in closed-loop systems

DEFF Research Database (Denmark)

Sekunda, André Krabdrup; Niemann, Hans Henrik; Poulsen, Niels Kjølstad

2018-01-01

Fault diagnosis of closed-loop systems is extremely relevant for high-precision equipment and safety critical systems. Fault diagnosis is usually divided into 2 schemes: active and passive fault diagnosis. Recent studies have highlighted some advantages of active fault diagnosis based on dual Youla......-Jabr-Bongiorno-Kucera parameters. In this paper, a method for closed-loop active fault diagnosis based on statistical detectors is given using dual Youla-Jabr-Bongiorno-Kucera parameters. The goal of this paper is 2-fold. First, the authors introduce a method for measuring a residual signal subject to white noise. Second...

A Textual Case-Based Mobile Phone Diagnosis Support System ...

African Journals Online (AJOL)

In this paper, a Mobile Phone Diagnosis Support System is presented as an extension to jCOLIBRI which accepts a problem and reasons with cases to provide a solution related to a new given problem. Experimental evaluation using some set of problems shows that the developed system predicts the solution that is ...

SENSORS FAULT DIAGNOSIS ALGORITHM DESIGN OF A HYDRAULIC SYSTEM

Directory of Open Access Journals (Sweden)

Matej ORAVEC

2017-06-01

Full Text Available This article presents the sensors fault diagnosis system design for the hydraulic system, which is based on the group of the three fault estimation filters. These filters are used for estimation of the system states and sensors fault magnitude. Also, this article briefly stated the hydraulic system state control design with integrator, which is important assumption for the fault diagnosis system design. The sensors fault diagnosis system is implemented into the Matlab/Simulink environment and it is verified using the controlled hydraulic system simulation model. Verification of the designed fault diagnosis system is realized by series of experiments, which simulates sensors faults. The results of the experiments are briefly presented in the last part of this article.

A Learning Health Care System Using Computer-Aided Diagnosis.

Science.gov (United States)

Cahan, Amos; Cimino, James J

2017-03-08

Physicians intuitively apply pattern recognition when evaluating a patient. Rational diagnosis making requires that clinical patterns be put in the context of disease prior probability, yet physicians often exhibit flawed probabilistic reasoning. Difficulties in making a diagnosis are reflected in the high rates of deadly and costly diagnostic errors. Introduced 6 decades ago, computerized diagnosis support systems are still not widely used by internists. These systems cannot efficiently recognize patterns and are unable to consider the base rate of potential diagnoses. We review the limitations of current computer-aided diagnosis support systems. We then portray future diagnosis support systems and provide a conceptual framework for their development. We argue for capturing physician knowledge using a novel knowledge representation model of the clinical picture. This model (based on structured patient presentation patterns) holds not only symptoms and signs but also their temporal and semantic interrelations. We call for the collection of crowdsourced, automatically deidentified, structured patient patterns as means to support distributed knowledge accumulation and maintenance. In this approach, each structured patient pattern adds to a self-growing and -maintaining knowledge base, sharing the experience of physicians worldwide. Besides supporting diagnosis by relating the symptoms and signs with the final diagnosis recorded, the collective pattern map can also provide disease base-rate estimates and real-time surveillance for early detection of outbreaks. We explain how health care in resource-limited settings can benefit from using this approach and how it can be applied to provide feedback-rich medical education for both students and practitioners. ©Amos Cahan, James J Cimino. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 08.03.2017.

Development and realization of the open fault diagnosis system based on XPE

Science.gov (United States)

Deng, Hui; Wang, TaiYong; He, HuiLong; Xu, YongGang; Zeng, JuXiang

2005-12-01

To make the complex mechanical equipment work in good service, the technology for realizing an embedded open system is introduced systematically, including open hardware configuration, customized embedded operation system and open software structure. The ETX technology is adopted in this system, integrating the CPU main-board functions, and achieving the quick, real-time signal acquisition and intelligent data analysis with applying DSP and CPLD data acquisition card. Under the open configuration, the signal bus mode such as PCI, ISA and PC/104 can be selected and the styles of the signals can be chosen too. In addition, through customizing XPE system, adopting the EWF (Enhanced Write Filter), and realizing the open system authentically, the stability of the system is enhanced. Multi-thread and multi-task programming techniques are adopted in the software programming process. Interconnecting with the remote fault diagnosis center via the net interface, cooperative diagnosis is conducted and the intelligent degree of the fault diagnosis is improved.

All Roads Lead to Fault Diagnosis : Model-Based Reasoning with LYDIA

NARCIS (Netherlands)

Feldman, A.B.; Pietersma, J.; Van Gemund, A.J.C.

2006-01-01

Model-Based Reasoning (MBR) over qualitative models of complex, real-world systems has proven succesful for automated fault diagnosis, control, and repair. Expressing a system under diagnosis in a formal model and infering a diagnosis given observations are both challenging problems. In this paper

A web-based knowledge management system integrating Western and Traditional Chinese Medicine for relational medical diagnosis.

Science.gov (United States)

Herrera-Hernandez, Maria C; Lai-Yuen, Susana K; Piegl, Les A; Zhang, Xiao

2016-10-26

This article presents the design of a web-based knowledge management system as a training and research tool for the exploration of key relationships between Western and Traditional Chinese Medicine, in order to facilitate relational medical diagnosis integrating these mainstream healing modalities. The main goal of this system is to facilitate decision-making processes, while developing skills and creating new medical knowledge. Traditional Chinese Medicine can be considered as an ancient relational knowledge-based approach, focusing on balancing interrelated human functions to reach a healthy state. Western Medicine focuses on specialties and body systems and has achieved advanced methods to evaluate the impact of a health disorder on the body functions. Identifying key relationships between Traditional Chinese and Western Medicine opens new approaches for health care practices and can increase the understanding of human medical conditions. Our knowledge management system was designed from initial datasets of symptoms, known diagnosis and treatments, collected from both medicines. The datasets were subjected to process-oriented analysis, hierarchical knowledge representation and relational database interconnection. Web technology was implemented to develop a user-friendly interface, for easy navigation, training and research. Our system was prototyped with a case study on chronic prostatitis. This trial presented the system's capability for users to learn the correlation approach, connecting knowledge in Western and Traditional Chinese Medicine by querying the database, mapping validated medical information, accessing complementary information from official sites, and creating new knowledge as part of the learning process. By addressing the challenging tasks of data acquisition and modeling, organization, storage and transfer, the proposed web-based knowledge management system is presented as a tool for users in medical training and research to explore, learn and

Model-based energy monitoring and diagnosis of telecommunication cooling systems

International Nuclear Information System (INIS)

Sorrentino, Marco; Acconcia, Matteo; Panagrosso, Davide; Trifirò, Alena

2016-01-01

A methodology is proposed for on-line monitoring of cooling load supplied by Telecommunication (TLC) cooling systems. Sensible cooling load is estimated via a proportional integral controller-based input estimator, whereas a lumped parameters model was developed aiming at estimating air handling units (AHUs) latent heat load removal. The joint deployment of above estimators enables accurate prediction of total cooling load, as well as of related AHUs and free-coolers energy performance. The procedure was then proven effective when extended to cooling systems having a centralized chiller, through model-based estimation of a key performance metric, such as the energy efficiency ratio. The results and experimental validation presented throughout the paper confirm the suitability of the proposed procedure as a reliable and effective energy monitoring and diagnostic tool for TLC applications. Moreover, the proposed modeling approach, beyond its direct contribution towards smart use and conservation of energy, can be fruitfully deployed as a virtual sensor of removed heat load into a variety of residential and industrial applications. - Highlights: • Accurate cooling load prediction in telecommunication rooms. • Development of an input-estimator for sensible cooling load simulation. • Model-based estimation of latent cooling load. • Model-based prediction of centralized chiller energy performance in central offices. • Diagnosis-oriented application of proposed cooling load estimator.

Development of distributed plant monitoring and diagnosis system at Monju

International Nuclear Information System (INIS)

Okusa, Kyoichi; Tamayama, Kiyoshi; Kitamura, Tomomi

2003-01-01

In a nuclear plant, it is required to detect an anomaly as early as possible and to inhibit adverse consequences. This requirement is especially important for a prototype Fast Breeder Reactor Monju. Therefore, a monitoring and diagnosis system is required to be developed for Monju plant equipments. In these days, such a monitoring and diagnosis system can be realized using Web technology with rationalized system resources due to the remarkable progress of computer network technology. Then, we developed a Web based platform for the monitoring and diagnosis system of Monju. Distributed architecture, standardization and highly flexible system structure have been taken account of in the development. This newly developed platform and prototype monitoring and diagnosis systems have been validated. Prototype monitoring and diagnosis systems on the platform acquire Monju plant data and display the data on client computers using Monju intranet with acceptable delay times. The prototype monitoring and diagnosis systems for Monju have been developed on the platform and the whole system has been validated. (author)

Mobile Clinical Decision Support System for Acid-base Balance Diagnosis and Treatment Recommendation.

Science.gov (United States)

Mandzuka, Mensur; Begic, Edin; Boskovic, Dusanka; Begic, Zijo; Masic, Izet

2017-06-01

This paper presents mobile application implementing a decision support system for acid-base disorder diagnosis and treatment recommendation. The application was developed using the official integrated development environment for the Android platform (to maximize availability and minimize investments in specialized hardware) called Android Studio. The application identifies disorder, based on the blood gas analysis, evaluates whether the disorder has been compensated, and based on additional input related to electrolyte imbalance, provides recommendations for treatment. The application is a tool in the hands of the user, which provides assistance during acid-base disorders treatment. The application will assist the physician in clinical practice and is focused on the treatment in intensive care.

Research of Litchi Diseases Diagnosis Expertsystem Based on Rbr and Cbr

Science.gov (United States)

Xu, Bing; Liu, Liqun

To conquer the bottleneck problems existing in the traditional rule-based reasoning diseases diagnosis system, such as low reasoning efficiency and lack of flexibility, etc.. It researched the integrated case-based reasoning (CBR) and rule-based reasoning (RBR) technology, and put forward a litchi diseases diagnosis expert system (LDDES) with integrated reasoning method. The method use data mining and knowledge obtaining technology to establish knowledge base and case library. It adopt rules to instruct the retrieval and matching for CBR, and use association rule and decision trees algorithm to calculate case similarity.The experiment shows that the method can increase the system's flexibility and reasoning ability, and improve the accuracy of litchi diseases diagnosis.

Fault diagnosis for discrete event systems: Modelling and verification

International Nuclear Information System (INIS)

Simeu-Abazi, Zineb; Di Mascolo, Maria; Knotek, Michal

2010-01-01

This paper proposes an effective way for diagnosis of discrete-event systems using a timed-automaton. It is based on the model-checking technique, thanks to time analysis of the timed model. The paper proposes a method to construct all the timed models and details the different steps used to obtain the diagnosis path. A dynamic model with temporal transitions is proposed in order to model the system. By 'dynamical model', we mean an extension of timed automata for which the faulty states are identified. The model of the studied system contains the faultless functioning states and all the faulty states. Our method is based on the backward exploitation of the dynamic model, where all possible reverse paths are searched. The reverse path is the connection of the faulty state to the initial state. The diagnosis method is based on the coherence between the faulty occurrence time and the reverse path length. A real-world batch process is used to demonstrate the modelling steps and the proposed backward time analysis method to reach the diagnosis results.

Information Based Fault Diagnosis

DEFF Research Database (Denmark)

Niemann, Hans Henrik; Poulsen, Niels Kjølstad

2008-01-01

Fault detection and isolation, (FDI) of parametric faults in dynamic systems will be considered in this paper. An active fault diagnosis (AFD) approach is applied. The fault diagnosis will be investigated with respect to different information levels from the external inputs to the systems. These ...

The Intelligent System of Cardiovascular Disease Diagnosis Based on Extension Data Mining

Science.gov (United States)

Sun, Baiqing; Li, Yange; Zhang, Lin

This thesis gives the general definition of the concepts of extension knowledge, extension data mining and extension data mining theorem in high dimension space, and also builds the IDSS integrated system by the rough set, expert system and neural network, develops the relevant computer software. From the diagnosis tests, according to the common diseases of myocardial infarctions, angina pectoris and hypertension, and made the test result with physicians, the results shows that the sensitivity, specific and accuracy diagnosis by the IDSS are all higher than the physicians. It can improve the rate of the accuracy diagnosis of physician with the auxiliary help of this system, which have the obvious meaning in low the mortality, disability rate and high the survival rate, and has strong practical values and further social benefits.

A Privacy-Preserving Intelligent Medical Diagnosis System Based on Oblivious Keyword Search

Directory of Open Access Journals (Sweden)

Zhaowen Lin

2017-01-01

Full Text Available One of the concerns people have is how to get the diagnosis online without privacy being jeopardized. In this paper, we propose a privacy-preserving intelligent medical diagnosis system (IMDS, which can efficiently solve the problem. In IMDS, users submit their health examination parameters to the server in a protected form; this submitting process is based on Paillier cryptosystem and will not reveal any information about their data. And then the server retrieves the most likely disease (or multiple diseases from the database and returns it to the users. In the above search process, we use the oblivious keyword search (OKS as a basic framework, which makes the server maintain the computational ability but cannot learn any personal information over the data of users. Besides, this paper also provides a preprocessing method for data stored in the server, to make our protocol more efficient.

Diagnosis of wind turbine rotor system

DEFF Research Database (Denmark)

Niemann, Hans Henrik; Mirzaei, Mahmood; Henriksen, Lars Christian

2016-01-01

is based on available standard sensors on wind turbines. The method can be used both on-line as well as off-line. Faults or changes in the rotor system will result in asymmetries, which can be monitored and diagnosed. This can be done by using the multi-blade coordinate transformation. Changes in the rotor......This paper describes a model free method for monitoring and fault diagnosis of the elements in a rotor system for a wind turbine. The diagnosis as well as the monitoring is done without using any model of the wind turbine and the applied controller or a description of the wind profile. The method...

Gene expression-based molecular diagnostic system for malignant gliomas is superior to histological diagnosis.

Science.gov (United States)

Shirahata, Mitsuaki; Iwao-Koizumi, Kyoko; Saito, Sakae; Ueno, Noriko; Oda, Masashi; Hashimoto, Nobuo; Takahashi, Jun A; Kato, Kikuya

2007-12-15

Current morphology-based glioma classification methods do not adequately reflect the complex biology of gliomas, thus limiting their prognostic ability. In this study, we focused on anaplastic oligodendroglioma and glioblastoma, which typically follow distinct clinical courses. Our goal was to construct a clinically useful molecular diagnostic system based on gene expression profiling. The expression of 3,456 genes in 32 patients, 12 and 20 of whom had prognostically distinct anaplastic oligodendroglioma and glioblastoma, respectively, was measured by PCR array. Next to unsupervised methods, we did supervised analysis using a weighted voting algorithm to construct a diagnostic system discriminating anaplastic oligodendroglioma from glioblastoma. The diagnostic accuracy of this system was evaluated by leave-one-out cross-validation. The clinical utility was tested on a microarray-based data set of 50 malignant gliomas from a previous study. Unsupervised analysis showed divergent global gene expression patterns between the two tumor classes. A supervised binary classification model showed 100% (95% confidence interval, 89.4-100%) diagnostic accuracy by leave-one-out cross-validation using 168 diagnostic genes. Applied to a gene expression data set from a previous study, our model correlated better with outcome than histologic diagnosis, and also displayed 96.6% (28 of 29) consistency with the molecular classification scheme used for these histologically controversial gliomas in the original article. Furthermore, we observed that histologically diagnosed glioblastoma samples that shared anaplastic oligodendroglioma molecular characteristics tended to be associated with longer survival. Our molecular diagnostic system showed reproducible clinical utility and prognostic ability superior to traditional histopathologic diagnosis for malignant glioma.

Fault diagnosis for dynamic power system

International Nuclear Information System (INIS)

Thabet, A.; Abdelkrim, M.N.; Boutayeb, M.; Didier, G.; Chniba, S.

2011-01-01

The fault diagnosis problem for dynamic power systems is treated, the nonlinear dynamic model based on a differential algebraic equations is transformed with reduced index to a simple dynamic model. Two nonlinear observers are used for generating the fault signals for comparison purposes, one of them being an extended Kalman estimator and the other a new extended kalman filter with moving horizon with a study of convergence based on the choice of matrix of covariance of the noises of system and measurements. The paper illustrates a simulation study applied on IEEE 3 buses test system.

Design of dynamic power quality monitoring and fault diagnosis system of ship-power system based on Ethernet

Directory of Open Access Journals (Sweden)

HU Hongqian

2018-02-01

Full Text Available [Objectives] According to situation that the ship power information exchange system based on the traditional field bus has been unable to meet the needs of modern ship power system for informatization, automation, intelligent and safe operation. [Methods] This paper proposes the use of industrial Ethernet Modbus/TCP to make up for lack of field-bus. Then, the data center is established by collecting the inherent data of the field bus of the combined ship power system and collecting the real-time data from the online measurement device based on the Modbus/TCP. Correlation theory and neural network intelligent algorithm are used to analyze big data to complete the dynamic power quality monitoring and fault diagnosis of ship power system. [Results] Finally, the man-machine interface is designed with LabVIEW. [Conclusions] The feasibility of the software and hardware implementation of the scheme is verified by the laboratory platform.

Expert environment for the development of nuclear power plants failure diagnosis systems

International Nuclear Information System (INIS)

Guido, P.N.; Oggianu, S.; Etchepareborda, A.; Fernandez, O.

1996-01-01

The present work explores some of the developing stages of an Expert Environment for plant failures Diagnosis Systems starting from Knowledge Based Systems. We present a prototype that carries out an inspection of anomalous symptoms and a diagnosis process based on a Plant Abnormality Model of a PHWR secondary system

Application of ENN-1 for Fault Diagnosis of Wind Power Systems

Directory of Open Access Journals (Sweden)

Meng-Hui Wang

2012-01-01

Full Text Available Maintaining a wind turbine and ensuring secure is not easy because of long-term exposure to the environment and high installation locations. Wind turbines need fully functional condition-monitoring and fault diagnosis systems that prevent accidents and reduce maintenance costs. This paper presents a simulator design for fault diagnosis of wind power systems and further proposes some fault diagnosis technologies such as signal analysis, feature selecting, and diagnosis methods. First, this paper uses a wind power simulator to produce fault conditions and features from the monitoring sensors. Then an extension neural network type-1- (ENN-1- based method is proposed to develop the core of the fault diagnosis system. The proposed system will benefit the development of real fault diagnosis systems with testing models that demonstrate satisfactory results.

Fault diagnosis and fault-tolerant control based on adaptive control approach

CERN Document Server

Shen, Qikun; Shi, Peng

2017-01-01

This book provides recent theoretical developments in and practical applications of fault diagnosis and fault tolerant control for complex dynamical systems, including uncertain systems, linear and nonlinear systems. Combining adaptive control technique with other control methodologies, it investigates the problems of fault diagnosis and fault tolerant control for uncertain dynamic systems with or without time delay. As such, the book provides readers a solid understanding of fault diagnosis and fault tolerant control based on adaptive control technology. Given its depth and breadth, it is well suited for undergraduate and graduate courses on linear system theory, nonlinear system theory, fault diagnosis and fault tolerant control techniques. Further, it can be used as a reference source for academic research on fault diagnosis and fault tolerant control, and for postgraduates in the field of control theory and engineering. .

Diagnosis aids with artificial intelligence in the PSAD system

International Nuclear Information System (INIS)

Dourgnon-Hanoune, A.; Porcheron, M.; Ricard, B.

1996-01-01

To improve monitoring and diagnosis capabilities in nuclear power plants, Electricite de France (EDF) has designed an integrated monitoring and diagnosis assistance system: PSAD - Poste de Surveillance et d'Aide au Diagnostic. The development of this sophisticated monitoring and data processing system requires the addition of analysis and diagnosis assistance capabilities. Diagnostic knowledge based systems have thus been added to the functions monitored in PSAD: DIVA for turbine generators, and DIAPO for reactor coolant pumps. These systems rely on a representation of the diagnostic reasoning process of experts and of supporting knowledge. Diagnosis in both systems is performed through an abductive reasoning process applied to component fault models and observations derived from their actual behavior, as provided by the monitoring functions. The basic theoretical elements of this diagnostic model are summarized in a first part of this paper. In a second part, DIVA and DIAPO specific elements are described. (authors)

A Structural Model Decomposition Framework for Hybrid Systems Diagnosis

Science.gov (United States)

Daigle, Matthew; Bregon, Anibal; Roychoudhury, Indranil

2015-01-01

Nowadays, a large number of practical systems in aerospace and industrial environments are best represented as hybrid systems that consist of discrete modes of behavior, each defined by a set of continuous dynamics. These hybrid dynamics make the on-line fault diagnosis task very challenging. In this work, we present a new modeling and diagnosis framework for hybrid systems. Models are composed from sets of user-defined components using a compositional modeling approach. Submodels for residual generation are then generated for a given mode, and reconfigured efficiently when the mode changes. Efficient reconfiguration is established by exploiting causality information within the hybrid system models. The submodels can then be used for fault diagnosis based on residual generation and analysis. We demonstrate the efficient causality reassignment, submodel reconfiguration, and residual generation for fault diagnosis using an electrical circuit case study.

Organizational Diagnosis in Project-Based Companies

Directory of Open Access Journals (Sweden)

Behrouz Zarei

2014-05-01

Full Text Available The purpose of this article is to develop a new method for corporate diagnosis (CD. To this end, a method is developed for the diagnosis process of project-based companies. The article presents a case study in a large company where data have been collected through focus groups. Project delay, high project cost, and low profitability are examples of project deficiency in project-based companies. Such issues have made managers pay special attention to find effective solutions to improve them. Prominent factors are inappropriate strategy, structure, system, human resource management, and PMBOK(Project Management Body of Knowledge processes. Thus, CD and analysis is an important task in improvement of corporate performance. The CD model that is developed in this article could be used for project-based companies. The proposed method can be used for CD in any project-based company. This article provides an emphatic application of CD as a prerequisite for restructuring in project-based companies.

Design of a fault diagnosis system for next generation nuclear power plants

International Nuclear Information System (INIS)

Zhao, K.; Upadhyaya, B.R.; Wood, R.T.

2004-01-01

A new design approach for fault diagnosis is developed for next generation nuclear power plants. In the nuclear reactor design phase, data reconciliation is used as an efficient tool to determine the measurement requirements to achieve the specified goal of fault diagnosis. In the reactor operation phase, the plant measurements are collected to estimate uncertain model parameters so that a high fidelity model can be obtained for fault diagnosis. The proposed algorithm of fault detection and isolation is able to combine the strength of first principle model based fault diagnosis and the historical data based fault diagnosis. Principal component analysis on the reconciled data is used to develop a statistical model for fault detection. The updating of the principal component model based on the most recent reconciled data is a locally linearized model around the current plant measurements, so that it is applicable to any generic nonlinear systems. The sensor fault diagnosis and process fault diagnosis are decoupled through considering the process fault diagnosis as a parameter estimation problem. The developed approach has been applied to the IRIS helical coil steam generator system to monitor the operational performance of individual steam generators. This approach is general enough to design fault diagnosis systems for the next generation nuclear power plants. (authors)

[A computer-aided image diagnosis and study system].

Science.gov (United States)

Li, Zhangyong; Xie, Zhengxiang

2004-08-01

The revolution in information processing, particularly the digitizing of medicine, has changed the medical study, work and management. This paper reports a method to design a system for computer-aided image diagnosis and study. Combined with some good idea of graph-text system and picture archives communicate system (PACS), the system was realized and used for "prescription through computer", "managing images" and "reading images under computer and helping the diagnosis". Also typical examples were constructed in a database and used to teach the beginners. The system was developed by the visual developing tools based on object oriented programming (OOP) and was carried into operation on the Windows 9X platform. The system possesses friendly man-machine interface.

Artificial intelligence tools decision support systems in condition monitoring and diagnosis

CERN Document Server

Galar Pascual, Diego

2015-01-01

Artificial Intelligence Tools: Decision Support Systems in Condition Monitoring and Diagnosis discusses various white- and black-box approaches to fault diagnosis in condition monitoring (CM). This indispensable resource: Addresses nearest-neighbor-based, clustering-based, statistical, and information theory-based techniques Considers the merits of each technique as well as the issues associated with real-life application Covers classification methods, from neural networks to Bayesian and support vector machines Proposes fuzzy logic to explain the uncertainties associated with diagnostic processes Provides data sets, sample signals, and MATLAB® code for algorithm testing Artificial Intelligence Tools: Decision Support Systems in Condition Monitoring and Diagnosis delivers a thorough evaluation of the latest AI tools for CM, describing the most common fault diagnosis techniques used and the data acquired when these techniques are applied.

Usability evaluation of a web-based support system for people with a schizophrenia diagnosis.

Science.gov (United States)

van der Krieke, Lian; Emerencia, Ando C; Aiello, Marco; Sytema, Sjoerd

2012-02-06

Routine Outcome Monitoring (ROM) is a systematic way of assessing service users' health conditions for the purpose of better aiding their care. ROM consists of various measures used to assess a service user's physical, psychological, and social condition. While ROM is becoming increasingly important in the mental health care sector, one of its weaknesses is that ROM is not always sufficiently service user-oriented. First, clinicians tend to concentrate on those ROM results that provide information about clinical symptoms and functioning, whereas it has been suggested that a service user-oriented approach needs to focus on personal recovery. Second, service users have limited access to ROM results and they are often not equipped to interpret them. These problems need to be addressed, as access to resources and the opportunity to share decision making has been indicated as a prerequisite for service users to become a more equal partner in communication with their clinicians. Furthermore, shared decision making has been shown to improve the therapeutic alliance and to lead to better care. Our aim is to build a web-based support system which makes ROM results more accessible to service users and to provide them with more concrete and personalized information about their functioning (ie, symptoms, housing, social contacts) that they can use to discuss treatment options with their clinician. In this study, we will report on the usability of the web-based support system for service users with schizophrenia. First, we developed a prototype of a web-based support system in a multidisciplinary project team, including end-users. We then conducted a usability study of the support system consisting of (1) a heuristic evaluation, (2) a qualitative evaluation and (3) a quantitative evaluation. Fifteen service users with a schizophrenia diagnosis and four information and communication technology (ICT) experts participated in the study. The results show that people with a

Parasite-based malaria diagnosis: are health systems in Uganda equipped enough to implement the policy?

Science.gov (United States)

Kyabayinze, Daniel J; Achan, Jane; Nakanjako, Damalie; Mpeka, Betty; Mawejje, Henry; Mugizi, Rukaaka; Kalyango, Joan N; D'Alessandro, Umberto; Talisuna, Ambrose; Jean-Pierre, Van geertruyden

2012-08-24

Malaria case management is a key strategy for malaria control. Effective coverage of parasite-based malaria diagnosis (PMD) remains limited in malaria endemic countries. This study assessed the health system's capacity to absorb PMD at primary health care facilities in Uganda. In a cross sectional survey, using multi-stage cluster sampling, lower level health facilities (LLHF) in 11 districts in Uganda were assessed for 1) tools, 2) skills, 3) staff and infrastructure, and 4) structures, systems and roles necessary for the implementing of PMD. Tools for PMD (microscopy and/or RDTs) were available at 30 (24%) of the 125 LLHF. All LLHF had patient registers and 15% had functional in-patient facilities. Three months' long stock-out periods were reported for oral and parenteral quinine at 39% and 47% of LLHF respectively. Out of 131 health workers interviewed, 86 (66%) were nursing assistants; 56 (43%) had received on-job training on malaria case management and 47 (36%) had adequate knowledge in malaria case management. Overall, only 18% (131/730) Ministry of Health approved staff positions were filled by qualified personnel and 12% were recruited or transferred within six months preceding the survey. Of 186 patients that received referrals from LLHF, 130(70%) had received pre-referral anti-malarial drugs, none received pre-referral rectal artesunate and 35% had been referred due to poor response to antimalarial drugs. Primary health care facilities had inadequate human and infrastructural capacity to effectively implement universal parasite-based malaria diagnosis. The priority capacity building needs identified were: 1) recruitment and retention of qualified staff, 2) comprehensive training of health workers in fever management, 3) malaria diagnosis quality control systems and 4) strengthening of supply chain, stock management and referral systems.

PSG-EXPERT. An expert system for the diagnosis of sleep disorders.

Science.gov (United States)

Fred, A; Filipe, J; Partinen, M; Paiva, T

2000-01-01

This paper describes PSG-EXPERT, an expert system in the domain of sleep disorders exploring polysomnographic data. The developed software tool is addressed from two points of view: (1)--as an integrated environment for the development of diagnosis-oriented expert systems; (2)--as an auxiliary diagnosis tool in the particular domain of sleep disorders. Developed over a Windows platform, this software tool extends one of the most popular shells--CLIPS (C Language Integrated Production System) with the following features: backward chaining engine; graph-based explanation facilities; knowledge editor including a fuzzy fact editor and a rules editor, with facts-rules integrity checking; belief revision mechanism; built-in case generator and validation module. It therefore provides graphical support for knowledge acquisition, edition, explanation and validation. From an application domain point of view, PSG-Expert is an auxiliary diagnosis system for sleep disorders based on polysomnographic data, that aims at assisting the medical expert in his diagnosis task by providing automatic analysis of polysomnographic data, summarising the results of this analysis in terms of a report of major findings and possible diagnosis consistent with the polysomnographic data. Sleep disorders classification follows the International Classification of Sleep Disorders. Major features of the system include: browsing on patients data records; structured navigation on Sleep Disorders descriptions according to ASDA definitions; internet links to related pages; diagnosis consistent with polysomnographic data; graphical user-interface including graph-based explanatory facilities; uncertainty modelling and belief revision; production of reports; connection to remote databases.

Data-driven design of fault diagnosis systems nonlinear multimode processes

CERN Document Server

Haghani Abandan Sari, Adel

2014-01-01

In many industrial applications early detection and diagnosis of abnormal behavior of the plant is of great importance. During the last decades, the complexity of process plants has been drastically increased, which imposes great challenges in development of model-based monitoring approaches and it sometimes becomes unrealistic for modern large-scale processes. The main objective of Adel Haghani Abandan Sari is to study efficient fault diagnosis techniques for complex industrial systems using process historical data and considering the nonlinear behavior of the process. To this end, different methods are presented to solve the fault diagnosis problem based on the overall behavior of the process and its dynamics. Moreover, a novel technique is proposed for fault isolation and determination of the root-cause of the faults in the system, based on the fault impacts on the process measurements. Contents Process monitoring Fault diagnosis and fault-tolerant control Data-driven approaches and decision making Target...

Automatic Vertebral Fracture Assessment System (AVFAS) for Spinal Pathologies Diagnosis Based on Radiograph X-Ray Images

Science.gov (United States)

Mustapha, Aouache; Hussain, Aini; Samad, Salina Abd; Bin Abdul Hamid, Hamzaini; Ariffin, Ahmad Kamal

Nowadays, medical imaging has become a major tool in many clinical trials. This is because the technology enables rapid diagnosis with visualization and quantitative assessment that facilitate health practitioners or professionals. Since the medical and healthcare sector is a vast industry that is very much related to every citizen's quality of life, the image based medical diagnosis has become one of the important service areas in this sector. As such, a medical diagnostic imaging (MDI) software tool for assessing vertebral fracture is being developed which we have named as AVFAS short for Automatic Vertebral Fracture Assessment System. The developed software system is capable of indexing, detecting and classifying vertebral fractures by measuring the shape and appearance of vertebrae of radiograph x-ray images of the spine. This paper describes the MDI software tool which consists of three main sub-systems known as Medical Image Training & Verification System (MITVS), Medical Image and Measurement & Decision System (MIMDS) and Medical Image Registration System (MIRS) in term of its functionality, performance, ongoing research and outstanding technical issues.

Failure diagnosis using deep belief learning based health state classification

International Nuclear Information System (INIS)

Tamilselvan, Prasanna; Wang, Pingfeng

2013-01-01

Effective health diagnosis provides multifarious benefits such as improved safety, improved reliability and reduced costs for operation and maintenance of complex engineered systems. This paper presents a novel multi-sensor health diagnosis method using deep belief network (DBN). DBN has recently become a popular approach in machine learning for its promised advantages such as fast inference and the ability to encode richer and higher order network structures. The DBN employs a hierarchical structure with multiple stacked restricted Boltzmann machines and works through a layer by layer successive learning process. The proposed multi-sensor health diagnosis methodology using DBN based state classification can be structured in three consecutive stages: first, defining health states and preprocessing sensory data for DBN training and testing; second, developing DBN based classification models for diagnosis of predefined health states; third, validating DBN classification models with testing sensory dataset. Health diagnosis using DBN based health state classification technique is compared with four existing diagnosis techniques. Benchmark classification problems and two engineering health diagnosis applications: aircraft engine health diagnosis and electric power transformer health diagnosis are employed to demonstrate the efficacy of the proposed approach

Fault Diagnosis in Dynamic Systems Using Fuzzy Interacting Observers

Directory of Open Access Journals (Sweden)

N. V. Kolesov

2013-01-01

Full Text Available A method of fault diagnosis in dynamic systems based on a fuzzy approach is proposed. The new method possesses two basic specific features which distinguish it from the other known fuzzy methods based on the application of fuzzy logic and a bank of state observers. First, this method uses a bank of interacting observers instead of traditional independent observers. The second specific feature of the proposed method is the assumption that there is no strict boundary between the serviceable and disabled technical states of the system, which makes it possible to specify a decision making rule for fault diagnosis.

Deductive Error Diagnosis and Inductive Error Generalization for Intelligent Tutoring Systems.

Science.gov (United States)

Hoppe, H. Ulrich

1994-01-01

Examines the deductive approach to error diagnosis for intelligent tutoring systems. Topics covered include the principles of the deductive approach to diagnosis; domain-specific heuristics to solve the problem of generalizing error patterns; and deductive diagnosis and the hypertext-based learning environment. (Contains 26 references.) (JLB)

Diagnosis of multi-agent systems and its application to public administration

NARCIS (Netherlands)

Boer, A.; van Engers, T.; Abramowicz, W.; Maciaszek, L.; Węcel, K.

2011-01-01

In this paper we present a model-based diagnosis view on the complex social systems in which large public administration organizations operate. The purpose of diagnosis as presented in this paper is to identify agent role instances that are not conforming to expectations in a multi-agent system

Monodetector system for diagnosis (DETEC)

International Nuclear Information System (INIS)

Alonso Abad, D.; Fernandez Paz, J.L.; Lopez Torres, O.M. and others

1997-01-01

Several clinical searches can be done using The Single Probe Diagnosis System: Thyroid uptake, Eritroferrocinetic studies, Studies of survival of hematite's, Studies of peripheral vascular diseases , Studies of gastric emptying time. The system can be set spectrometric parameters for several radionuclides ( 131I , 125I , 99mT c, 59F e, 51C r, 57G a, 57C o) used in Nuclear Medicine by itself. It is a unit made of a mechanical structure and a detection-measured system based in a Z80 microprocessor. Data obtained are processed and can be printed or sent to a P C by RS-232 protocol

Fault Diagnosis of Power Systems Using Intelligent Systems

Science.gov (United States)

Momoh, James A.; Oliver, Walter E. , Jr.

1996-01-01

The power system operator's need for a reliable power delivery system calls for a real-time or near-real-time Al-based fault diagnosis tool. Such a tool will allow NASA ground controllers to re-establish a normal or near-normal degraded operating state of the EPS (a DC power system) for Space Station Alpha by isolating the faulted branches and loads of the system. And after isolation, re-energizing those branches and loads that have been found not to have any faults in them. A proposed solution involves using the Fault Diagnosis Intelligent System (FDIS) to perform near-real time fault diagnosis of Alpha's EPS by downloading power transient telemetry at fault-time from onboard data loggers. The FDIS uses an ANN clustering algorithm augmented with a wavelet transform feature extractor. This combination enables this system to perform pattern recognition of the power transient signatures to diagnose the fault type and its location down to the orbital replaceable unit. FDIS has been tested using a simulation of the LeRC Testbed Space Station Freedom configuration including the topology from the DDCU's to the electrical loads attached to the TPDU's. FDIS will work in conjunction with the Power Management Load Scheduler to determine what the state of the system was at the time of the fault condition. This information is used to activate the appropriate diagnostic section, and to refine if necessary the solution obtained. In the latter case, if the FDIS reports back that it is equally likely that the faulty device as 'start tracker #1' and 'time generation unit,' then based on a priori knowledge of the system's state, the refined solution would be 'star tracker #1' located in cabinet ITAS2. It is concluded from the present studies that artificial intelligence diagnostic abilities are improved with the addition of the wavelet transform, and that when such a system such as FDIS is coupled to the Power Management Load Scheduler, a faulty device can be located and isolated

Model-based sensor diagnosis

International Nuclear Information System (INIS)

Milgram, J.; Dormoy, J.L.

1994-09-01

Running a nuclear power plant involves monitoring data provided by the installation's sensors. Operators and computerized systems then use these data to establish a diagnostic of the plant. However, the instrumentation system is complex, and is not immune to faults and failures. This paper presents a system for detecting sensor failures using a topological description of the installation and a set of component models. This model of the plant implicitly contains relations between sensor data. These relations must always be checked if all the components are functioning correctly. The failure detection task thus consists of checking these constraints. The constraints are extracted in two stages. Firstly, a qualitative model of their existence is built using structural analysis. Secondly, the models are formally handled according to the results of the structural analysis, in order to establish the constraints on the sensor data. This work constitutes an initial step in extending model-based diagnosis, as the information on which it is based is suspect. This work will be followed by surveillance of the detection system. When the instrumentation is assumed to be sound, the unverified constraints indicate errors on the plant model. (authors). 8 refs., 4 figs

Least-cost failure diagnosis in uncertain reliability systems

International Nuclear Information System (INIS)

Cox, Louis Anthony; Chiu, Steve Y.; Sun Xiaorong

1996-01-01

In many textbook solutions, for systems failure diagnosis problems studied using reliability theory and artificial intelligence, the prior probabilities of different failure states can be estimated and used to guide the sequential search for failed components after the whole system fails. In practice, however, both the component failure probabilities and the structure function of the system being examined--i.e., the mapping between the states of its components and the state of the system--may not be known with certainty. At best:, the probabilities of different hypothesized system descriptions, each specifying the component failure probabilities and the system's structure function, may be known to a useful approximation, perhaps based on sample data and previous experience. Cost-effective diagnosis of the system's failure state is then a challenging problem. Although the probabilities of component failures are aleatory, uncertainties about these probabilities and about the system structure function are epistemic. This paper examines how to make best use of both epistemic prior probabilities for system descriptions and the information gleaned from costly inspections of component states after the system fails, to minimize the average cost of identifying the failure state. Two approaches are introduced for systems dominated by aleatory uncertainties, one motivated by information theory and the other based on the idea of trying to prove a hypothesis about the identity of the failure state as efficiently as possible. While the general problem of cost-effective failure diagnosis is computationally intractable (NP-hard), both heuristics provide useful approximations on small to moderate sized problems and optimal results for certain common types of reliability systems, including series, parallel, parallel-series, and k-out-of-n systems. A hybrid heuristic that adaptively chooses which heuristic to apply next after any sequence of observations (component test results

Design of a system based on diffuse logic for the diagnosis of the epilepsy starting from the interpretation of the electroencephalogram

International Nuclear Information System (INIS)

Buitrago, Eder

2002-01-01

The purpose of this investigation was to design of a system based on Diffuse Logic for the diagnosis of the epilepsy starting from the electroencephalogram interpretation. To achieve the elaboration of the design of the system, they were carried out bibliographical consultations in different sources of information related with the topic, like interviews semi structured and structured to an intentional sample contained by a group of experts in the area of diagnostic of the epilepsy. These techniques contributed the necessary information to determine the current situation of the process of diagnostic of the epilepsy and the bases of the proposed system, as well as they allowed to determine the necessity and feasibility of the application of the Diffuse Logic in the diagnosis of the epilepsy. The proposal is presented like a simple useful tool for the experts in diagnostic, but it is not conceived to substitute the expert in its functions. The diagnosis processes are of complex type, and in great measure they are numerous the variables that intervene in them, are for this reason that the knowledge and the expert's abilities will be the determinant for the elaboration of the definitive diagnosis

A fault diagnosis system for nuclear power plant operation

International Nuclear Information System (INIS)

Ohga, Yukiharu; Hayashi, Yoshiharu; Yuchi, Hiroyuki; Utena, Shunsuke; Maeda, Akihiko

2002-01-01

A fault diagnosis system has been developed to support operators in nuclear power plants. In the system various methods are combined to get a diagnosis result which provides better detection sensitivity and result reliability. The system is composed of an anomaly detection part with diagnosis modules, an integration part which obtains the diagnosis result by combining results from each diagnosis module, and a prediction part with state prediction and estimation modules. For the anomaly detection part, three kinds of modules are prepared: plant signal processing, early fault detection and event identification modules. The plant signal processing module uses wavelet transform and chaos technologies as well as fast Fourier transform (FFT) to analyze vibration sensor signals and to detect signal anomaly. The early fault detection module uses the neural network model of a plant subprocess to estimate the process variable values assuming normal conditions, and to detect an anomaly by comparing the measured and estimated values. The event identification module identifies the kind of occurring event by using the neural network and knowledge processing. In the integration part the diagnosis is performed by using knowledge processing. The knowledge for diagnosis is structured based on the means-ends abstraction hierarchy to simplify knowledge input and maintenance. In the prediction part, the prediction module predicts the future changes of process variables and plant interlock statuses and the estimation module estimates the values of unmeasurable variables. A prototype system has been developed and the system performance was evaluated. The evaluation results show that the developed technologies are effective to improve the human-machine system for plant operation. (author)

A practical approach to the diagnosis of systemic amyloidoses.

Science.gov (United States)

Fernández de Larrea, Carlos; Verga, Laura; Morbini, Patrizia; Klersy, Catherine; Lavatelli, Francesca; Foli, Andrea; Obici, Laura; Milani, Paolo; Capello, Gian Luca; Paulli, Marco; Palladini, Giovanni; Merlini, Giampaolo

2015-04-02

Accurate diagnosis of systemic amyloidosis is necessary both for assessing the prognosis and for delineating the appropriate treatment. It is based on histologic evidence of amyloid deposits and characterization of the amyloidogenic protein. We prospectively evaluated the diagnostic performance of immunoelectron microscopy (IEM) of abdominal fat aspirates from 745 consecutive patients with suspected systemic amyloidoses. All cases were extensively investigated with clinical and laboratory data, with a follow-up of at least 18 months. The 423 (56.8%) cases with confirmed systemic forms were used to estimate the diagnostic performance of IEM. Compared with Congo-red-based light microscopy, IEM was equally sensitive (75% to 80%) but significantly more specific (100% vs 80%; P 99% of the cases. IEM of abdominal fat aspirates is an effective tool in the routine diagnosis of systemic amyloidoses. © 2015 by The American Society of Hematology.

Diagnosis and Management of Systemic Sclerosis: A Practical Approach.

Science.gov (United States)

Lee, Jason J; Pope, Janet E

2016-02-01

Systemic sclerosis is a devastating multisystem rheumatologic condition that is characterized by autoimmunity, tissue fibrosis, obliterative vasculopathy and inflammation. Clinical presentation and course of the condition vary greatly, which complicates both diagnosis and corresponding treatment. In this regard, recent advances in disease understanding, both clinically and biochemically, have led to newer classification criteria for systemic sclerosis that are more inclusive than ever before. Still, significant disease modifying therapies do not yet exist for most patients. Therefore, organ-based management strategies are employed and research has been directed within this paradigm focusing on either the most debilitating symptoms, such as Raynaud's phenomenon, digital ulcers and cutaneous sclerosis, or life-threatening organ involvement such as interstitial lung disease and pulmonary arterial hypertension. The current trends in systemic sclerosis diagnosis, evidence-based treatment recommendations and potential future directions in systemic sclerosis treatment are discussed.

Diagnosis and Prognostic of Wastewater Treatment System Based on Bayesian Network

Science.gov (United States)

Li, Dan; Yang, Haizhen; Liang, XiaoFeng

2010-11-01

Wastewater treatment is a complicated and dynamic process. The treatment effect can be influenced by many variables in microbial, chemical and physical aspects. These variables are always uncertain. Due to the complex biological reaction mechanisms, the highly time-varying and multivariable aspects, the diagnosis and prognostic of wastewater treatment system are still difficult in practice. Bayesian network (BN) is one of the best methods for dealing with uncertainty in the artificial intelligence field. Because of the powerful inference ability and convenient decision mechanism, BN can be employed into the model description and influencing factor analysis of wastewater treatment system with great flexibility and applicability.In this paper, taking modified sequencing batch reactor (MSBR) as an analysis object, BN model was constructed according to the influent water quality, operational condition and effluent effect data of MSBR, and then a novel approach based on BN is proposed to analyze the influencing factors of the wastewater treatment system. The approach presented gives an effective tool for diagnosing and predicting analysis of the wastewater treatment system. On the basis of the influent water quality and operational condition, effluent effect can be predicted. Moreover, according to the effluent effect, the influent water quality and operational condition also can be deduced.

Water quality diagnosis system for power plant

International Nuclear Information System (INIS)

Igarashi, Hiroo; Fukumoto, Toshihiko

1991-01-01

An AI diagnose system for the water quality control of a BWR type reactor is divided into a general diagnosing section for generally classifying the water quality conditions of the plant depending on a causal relation between the symptom of the water quality abnormality and its causes, generally diagnosing the position and the cause of the abnormality and ranking the items considered to be the cause, and a detail diagnosing section for a further diagnosis based on the result of the diagnosis in the former section. The general diagnosing section provides a plurality of threshold values showing the extent of the abnormality depending on the cause to the causal relation between the causes and the forecast events previously formed depending on the data of process sensors in the plant. Since the diagnosis for the abnormality and normality is given not only as an ON or OFF mode but also as the extent thereof, it can enter the detailed diagnosis in the most plausible order, based on a plurality of estimated causes, to enable to find the case and take a counter-measure in an early stage. (N.H.)

PACS-Based Computer-Aided Detection and Diagnosis

Science.gov (United States)

Huang, H. K. (Bernie); Liu, Brent J.; Le, Anh HongTu; Documet, Jorge

The ultimate goal of Picture Archiving and Communication System (PACS)-based Computer-Aided Detection and Diagnosis (CAD) is to integrate CAD results into daily clinical practice so that it becomes a second reader to aid the radiologist's diagnosis. Integration of CAD and Hospital Information System (HIS), Radiology Information System (RIS) or PACS requires certain basic ingredients from Health Level 7 (HL7) standard for textual data, Digital Imaging and Communications in Medicine (DICOM) standard for images, and Integrating the Healthcare Enterprise (IHE) workflow profiles in order to comply with the Health Insurance Portability and Accountability Act (HIPAA) requirements to be a healthcare information system. Among the DICOM standards and IHE workflow profiles, DICOM Structured Reporting (DICOM-SR); and IHE Key Image Note (KIN), Simple Image and Numeric Report (SINR) and Post-processing Work Flow (PWF) are utilized in CAD-HIS/RIS/PACS integration. These topics with examples are presented in this chapter.

A fundamental study on nuclear power plant diagnosis system

International Nuclear Information System (INIS)

Yoshimura, Sei-ichi; Fujimoto, Junzo

1987-01-01

Diagnosis of nuclear power plant is a large application field of knowledge engineering. But, the study examples are few and the diagnosis method is not established yet. This report describes the diagnosis method using cross correlation coefficients and describes the knowledge acquisition method of undefined transients in order to enhance the system performance. The usefulness of the system was verified by putting some data into the system. Main results are as follows. (1) Diagnosis method. Some transients are selected by the first judgement and one of them is identified by the second judgement using the cross correlation. (2) Knowledge aquisition method. When putting new data into the knowledge-base, the system indicates the inconsistency by arranging the aquired data, and the operators input new transient names and corresponding manipulation methods after analyzing the indicated results. (3) Usefulness of the system. Freedwater controller failures(2 transients), 2 recirculation pumps trip and a dummy datum combined 2 transients(one is feedwater controller failure and one is 2 recirculation pumps trip) were put into the system. It was proved that the system identified the transients correctly and it indicated the first hit and the inconsisency of the transients in the course of knowledge acquisition. (author)

An intelligent medical system for diagnosis of bone diseases

International Nuclear Information System (INIS)

Hatzilygeroudis, I.; Vassilakos, P.J.; Tsakalidis, A.

1994-01-01

In this paper, aspects of the design of an intelligent medical system for diagnosis of bone diseases that can be detected by scintigraphic images are presented. The system comprises three major parts: a user interface (UI), a database management system (DBMS), and an expert system (ES). The DBMS is used for manipulation of various patient data. A number of patient cases are selected as prototype and stored in separate database. Diagnosis is performed via the ES, called XBONE, based on patient data. Knowledge is represented via an integrated formalism that combines production rules and a neural network. This results in better representation, and facilitates knowledge acquisition and maintenance. (authors)

NN-Es Fault Diagnosis Method in Nuclear Power Equipment Based on Concept Lattice

International Nuclear Information System (INIS)

Liu Yongkuo; Xie Chunli; Xia Hong

2010-01-01

In order to improve the fault diagnosis accuracy of nuclear power plant,neural network and expert systems were combined to give full play to their advantages. In this paper, the concept lattice was applied to get the object properties, extracting the core attributes, dispensable attributes and relative necessary attributes from a large number raw data of fault symptoms.Based on these attributes, neural networks with different levels of importance were designed to improve the learning speed and diagnosis accuracy, and the diagnosis results of the neural networks were verified by using rule-based reasoning expert system. To verify the accuracy of this method, some simulation experiments about the typical faults of nuclear power plant were conducted. And the simulation results show that it is feasible to diagnose nuclear power plant faults with the confederation diagnosis methods combined the neural networks based on the concept lattice theory and expert system, with the distinctive features such as the efficiency of neural network learning, less calculation and reliability of diagnosis results and so on. (authors)

Decision Support System for Hepatitis Disease Diagnosis using Bayesian Network

Directory of Open Access Journals (Sweden)

Shamshad Lakho

2017-12-01

Full Text Available Medical judgments are tough and challenging as the decisions are often based on the deficient and ambiguous information. Moreover, the result of decision process has direct effects on human lives. The act of human decision declines in emergency situations due to the complication, time limit, and high risks. Therefore, provision of medical diagnosis plays a dynamic role, specifically in the preliminary stage when a physician has limited diagnosis experience and identifies the directions to be taken for the treatment process. Computerized Decision Support Systems have brought a revolution in the medical diagnosis. These automatic systems support the diagnosticians in the course of diagnosis. The major role of Decision Support Systems is to support the medical personnel in decision-making procedures regarding disease diagnosis and treatment recommendation. The proposed system provides easy support in Hepatitis disease recognition. The system is developed using the Bayesian network model. The physician provides the input to the system in the form of symptoms stated by the patient. These signs and symptoms match with the casual relationships present in the knowledge model. The Bayesian network infers conclusion from the knowledge model and calculates the probability of occurrence of Hepatitis B, C and D disorders.

Issues in practical model-based diagnosis

NARCIS (Netherlands)

Bakker, R.R.; Bakker, R.R.; van den Bempt, P.C.A.; van den Bempt, P.C.A.; Mars, Nicolaas; Out, D.-J.; Out, D.J.; van Soest, D.C.; van Soes, D.C.

1993-01-01

The model-based diagnosis project at the University of Twente has been directed at improving the practical usefulness of model-based diagnosis. In cooperation with industrial partners, the research addressed the modeling problem and the efficiency problem in model-based reasoning. Main results of

Combustion engine diagnosis model-based condition monitoring of gasoline and diesel engines and their components

CERN Document Server

Isermann, Rolf

2017-01-01

This book offers first a short introduction to advanced supervision, fault detection and diagnosis methods. It then describes model-based methods of fault detection and diagnosis for the main components of gasoline and diesel engines, such as the intake system, fuel supply, fuel injection, combustion process, turbocharger, exhaust system and exhaust gas aftertreatment. Additionally, model-based fault diagnosis of electrical motors, electric, pneumatic and hydraulic actuators and fault-tolerant systems is treated. In general series production sensors are used. It includes abundant experimental results showing the detection and diagnosis quality of implemented faults. Written for automotive engineers in practice, it is also of interest to graduate students of mechanical and electrical engineering and computer science. The Content Introduction.- I SUPERVISION, FAULT DETECTION AND DIAGNOSIS METHODS.- Supervision, Fault-Detection and Fault-Diagnosis Methods - a short Introduction.- II DIAGNOSIS OF INTERNAL COMBUST...

Diagnosis of dynamic systems based on explicit and implicit behavioural models: an application to gas turbines in Esprit Project Tiger

Energy Technology Data Exchange (ETDEWEB)

Trave-Massuyes, L. [Centre National de la Recherche Scientifique (CNRS), 31 - Toulouse (France); Milne, R.

1995-12-31

We are interested in the monitoring and diagnosis of dynamic systems. In our work, we are combining explicit temporal models of the behaviour of a dynamic system with implicit behavioural models supporting model based approaches. This work is drive by the needs of and applied to, two gas turbines of very different size and power. In this paper we describe the problems of building systems for these domains and illustrate how we have developed a system where these two approaches complement each other to provide a comprehensive fault detection and diagnosis system. We also explore the strengths and weaknesses of each approach. The work described here is currently working continuously, on line to a gas turbine in a major chemical plant. (author) 24 refs.

Diagnosis of dynamic systems based on explicit and implicit behavioural models: an application to gas turbines in Esprit Project Tiger

Energy Technology Data Exchange (ETDEWEB)

Trave-Massuyes, L [Centre National de la Recherche Scientifique (CNRS), 31 - Toulouse (France); Milne, R

1996-12-31

We are interested in the monitoring and diagnosis of dynamic systems. In our work, we are combining explicit temporal models of the behaviour of a dynamic system with implicit behavioural models supporting model based approaches. This work is drive by the needs of and applied to, two gas turbines of very different size and power. In this paper we describe the problems of building systems for these domains and illustrate how we have developed a system where these two approaches complement each other to provide a comprehensive fault detection and diagnosis system. We also explore the strengths and weaknesses of each approach. The work described here is currently working continuously, on line to a gas turbine in a major chemical plant. (author) 24 refs.

An intelligent medical system for diagnosis of bone diseases

Energy Technology Data Exchange (ETDEWEB)

Hatzilygeroudis, I [University of Patras, School of Engineering, Department of Computer Engineering and Informatics, 26500 Patras, Greece (Greece); Vassilakos, P J [Regional University Hospital of Patras, Department of Nuclear Medicine, Patras Greece (Greece); Tsakalidis, A [Computer Technology Institute, P.O. Box 1122, 26110 Patras, Greece (Greece)

1994-12-31

In this paper, aspects of the design of an intelligent medical system for diagnosis of bone diseases that can be detected by scintigraphic images are presented. The system comprises three major parts: a user interface (UI), a database management system (DBMS), and an expert system (ES). The DBMS is used for manipulation of various patient data. A number of patient cases are selected as prototype and stored in separate database. Diagnosis is performed via the ES, called XBONE, based on patient data. Knowledge is represented via an integrated formalism that combines production rules and a neural network. This results in better representation, and facilitates knowledge acquisition and maintenance. (authors). 10 refs., 2 figs.

Research and design of distributed intelligence fault diagnosis system in nuclear power plant

International Nuclear Information System (INIS)

Liu Yongkuo; Xie Chunli; Cheng Shouyu; Xia Hong

2011-01-01

In order to further reduce the misoperation after the faults occurring of nuclear power plant, according to the function distribution of nuclear power equipment and the distributed control features of digital instrument control system, a nuclear power plant distributed condition monitoring and fault diagnosis system was researched and designed. Based on decomposition-integrated diagnostic thinking, a fuzzy neural network and RBF neural network was presented to do the distributed local diagnosis and multi-source information fusion technology for the global integrated diagnosis. Simulation results show that the developed distributed status monitoring and fault diagnosis system can diagnose more typical accidents of PWR to provide effective diagnosis and operation information. (authors)

75 FR 35457 - Draft of the 2010 Causal Analysis/Diagnosis Decision Information System (CADDIS)

Science.gov (United States)

2010-06-22

... Causal Analysis/Diagnosis Decision Information System (CADDIS) AGENCY: Environmental Protection Agency... site, ``2010 release of the Causal Analysis/Diagnosis Decision Information System (CADDIS).'' The..., organize, and share information useful for causal evaluations in aquatic systems. CADDIS is based on EPA's...

Impact of the Japanese Diagnosis Procedure Combination-based Payment System on cardiovascular medicine-related costs.

Science.gov (United States)

Yasunaga, Hideo; Ide, Hiroo; Imamura, Tomoaki; Ohe, Kazuhiko

2005-09-01

In 2003, a lump-sum payment system based on Diagnosis Procedure Combinations (DPC) was introduced to 82 specific function hospitals in Japan. While the US DRG/PPS system is a "per case payment" system, the DPC based payment system adopts a "per day payment." It is generally believed that the Japanese system provides as much of an incentive as the DRG/PPS system to shorten the average length of stay (LOS). We performed an empirical analysis of the effect of LOS shortening on hospital revenue and expenditure under the DPC-based payment system, particularly in cardiovascular diseases. We also point out fundamentally controversial aspects of the current system. A total 109 cases were selected from patients hospitalized at the University of Tokyo Hospital from May to July, 2003 and classified into one of three categories: (1) cardiac catheter interventions, (2) cardiac catheter examinations, and (3) other conservative treatments. We analyzed the changes in profit per day in cases of a reduction in average LOS and an increase in the number of cases. In category (1) profit increased significantly in conjunction with reduced LOS. In category (2) profit increased only minimally. In category (3), profit increased rarely and sometimes decreased. In cases of conservative treatment, profits sometimes decreased because an increase in material costs exceeded the increase in revenue. It therefore became clear that the DPC-based payment system does not decisively provide an economic incentive to reduce LOS in cardiovascular medicine.

A noninvasive method for coronary artery diseases diagnosis using a clinically-interpretable fuzzy rule-based system

Directory of Open Access Journals (Sweden)

Hamid Reza Marateb

2015-01-01

Full Text Available Background: Coronary heart diseases/coronary artery diseases (CHDs/CAD, the most common form of cardiovascular disease (CVD, are a major cause for death and disability in developing/developed countries. CAD risk factors could be detected by physicians to prevent the CAD occurrence in the near future. Invasive coronary angiography, a current diagnosis method, is costly and associated with morbidity and mortality in CAD patients. The aim of this study was to design a computer-based noninvasive CAD diagnosis system with clinically interpretable rules. Materials and Methods: In this study, the Cleveland CAD dataset from the University of California UCI (Irvine was used. The interval-scale variables were discretized, with cut points taken from the literature. A fuzzy rule-based system was then formulated based on a neuro-fuzzy classifier (NFC whose learning procedure was speeded up by the scaled conjugate gradient algorithm. Two feature selection (FS methods, multiple logistic regression (MLR and sequential FS, were used to reduce the required attributes. The performance of the NFC (without/with FS was then assessed in a hold-out validation framework. Further cross-validation was performed on the best classifier. Results: In this dataset, 16 complete attributes along with the binary CHD diagnosis (gold standard for 272 subjects (68% male were analyzed. MLR + NFC showed the best performance. Its overall sensitivity, specificity, accuracy, type I error (α and statistical power were 79%, 89%, 84%, 0.1 and 79%, respectively. The selected features were "age and ST/heart rate slope categories," "exercise-induced angina status," fluoroscopy, and thallium-201 stress scintigraphy results. Conclusion: The proposed method showed "substantial agreement" with the gold standard. This algorithm is thus, a promising tool for screening CAD patients.

Method for fault diagnosis of digital control systems in nuclear power plant

International Nuclear Information System (INIS)

Suzuki, Satoshi; Nagaoka, Yukio; Ohga, Yukiharu; Ito, Tetsuo

1990-01-01

This paper presents a method for localizing faulty components of control systems by replaceable parts such as print boards and cables, in a large scale plant like a nuclear power plant. Most of today's control systems form a distributed configuration including many digital controllers interconnected by data communication networks. Usually, to localize the faulty components in nuclear plant control systems, suspected faulty components are narrowed down by executing manual tests to examine whether the objects are normal or abnormal based on design documents and personnel know-how, besides the uses of self-diagnosis functions built into the control systems. In the present method, procedures of various tests including the know-how and checking of self-diagnosis functions are provided as knowledge of tests. The tests to be executed is determined by considering failure probabilities of objects, and easiness and effectiveness of testing. Then, the suspects are narrowed down sequentially based on the test result. In checking feasibility of this diagnosis method for a simulated control system, intended faults are satisfactorily localized. This method is confirmed to be practicable for diagnosis of large scale digital control systems. (author)

Diagnosis in the Enterprise Management System

Directory of Open Access Journals (Sweden)

Skrynkovskyy Ruslan M.

2016-08-01

Full Text Available The aim of the article is to define the role and place of the diagnosis management system in the structure of the task system of the enterprise diagnosis. There suggested the essence of the concept of «diagnosis of the enterprise», which is understood as the process of identification, analysis and evaluation of the enterprise state and trends in its changes (changes of the state on the basis of relevant business indicators in order to develop recommendations on the elimination of problematic points and weaknesses in the functioning of the enterprise to ensure a qualitatively new level of its development and formation of prospects with consideration to the consequences of violation of the legislation in the field of economics and enterprise management and law (legal responsibility for the violation of the labor law, tax law, law on protection of economic competition, law on trade secret, etc.. It was found that the diagnosis in the system of enterprise management: 1 is a structural component (or a partial diagnosis task in a group of private diagnosis tasks in the system of diagnosis task of the enterprise activity; 2 as a sub-function of the control function (as a general function of management includes such components as: assessment (identification of key features, characteristics, parameters (indexes, indicators, properties; analysis (a thorough study of the structure, dynamics, trends, etc.; identification (involves determination of deviations of parameters from the criteria and/or standards, formulation of diagnosis. Prospects for further research in this direction are the development of methods for quantitative assessment of the effectiveness of the management system with the purpose of its introducing in practical activities of enterprises, namely in the processes of decision-making.

Fault diagnosis for the heat exchanger of the aircraft environmental control system based on the strong tracking filter.

Science.gov (United States)

Ma, Jian; Lu, Chen; Liu, Hongmei

2015-01-01

The aircraft environmental control system (ECS) is a critical aircraft system, which provides the appropriate environmental conditions to ensure the safe transport of air passengers and equipment. The functionality and reliability of ECS have received increasing attention in recent years. The heat exchanger is a particularly significant component of the ECS, because its failure decreases the system's efficiency, which can lead to catastrophic consequences. Fault diagnosis of the heat exchanger is necessary to prevent risks. However, two problems hinder the implementation of the heat exchanger fault diagnosis in practice. First, the actual measured parameter of the heat exchanger cannot effectively reflect the fault occurrence, whereas the heat exchanger faults are usually depicted by utilizing the corresponding fault-related state parameters that cannot be measured directly. Second, both the traditional Extended Kalman Filter (EKF) and the EKF-based Double Model Filter have certain disadvantages, such as sensitivity to modeling errors and difficulties in selection of initialization values. To solve the aforementioned problems, this paper presents a fault-related parameter adaptive estimation method based on strong tracking filter (STF) and Modified Bayes classification algorithm for fault detection and failure mode classification of the heat exchanger, respectively. Heat exchanger fault simulation is conducted to generate fault data, through which the proposed methods are validated. The results demonstrate that the proposed methods are capable of providing accurate, stable, and rapid fault diagnosis of the heat exchanger.

Fault diagnosis for the heat exchanger of the aircraft environmental control system based on the strong tracking filter.

Directory of Open Access Journals (Sweden)

Jian Ma

Full Text Available The aircraft environmental control system (ECS is a critical aircraft system, which provides the appropriate environmental conditions to ensure the safe transport of air passengers and equipment. The functionality and reliability of ECS have received increasing attention in recent years. The heat exchanger is a particularly significant component of the ECS, because its failure decreases the system's efficiency, which can lead to catastrophic consequences. Fault diagnosis of the heat exchanger is necessary to prevent risks. However, two problems hinder the implementation of the heat exchanger fault diagnosis in practice. First, the actual measured parameter of the heat exchanger cannot effectively reflect the fault occurrence, whereas the heat exchanger faults are usually depicted by utilizing the corresponding fault-related state parameters that cannot be measured directly. Second, both the traditional Extended Kalman Filter (EKF and the EKF-based Double Model Filter have certain disadvantages, such as sensitivity to modeling errors and difficulties in selection of initialization values. To solve the aforementioned problems, this paper presents a fault-related parameter adaptive estimation method based on strong tracking filter (STF and Modified Bayes classification algorithm for fault detection and failure mode classification of the heat exchanger, respectively. Heat exchanger fault simulation is conducted to generate fault data, through which the proposed methods are validated. The results demonstrate that the proposed methods are capable of providing accurate, stable, and rapid fault diagnosis of the heat exchanger.

Model-Based Sensor Placement for Component Condition Monitoring and Fault Diagnosis in Fossil Energy Systems

Energy Technology Data Exchange (ETDEWEB)

Mobed, Parham [Texas Tech Univ., Lubbock, TX (United States); Pednekar, Pratik [West Virginia Univ., Morgantown, WV (United States); Bhattacharyya, Debangsu [West Virginia Univ., Morgantown, WV (United States); Turton, Richard [West Virginia Univ., Morgantown, WV (United States); Rengaswamy, Raghunathan [Texas Tech Univ., Lubbock, TX (United States)

2016-01-29

Design and operation of energy producing, near “zero-emission” coal plants has become a national imperative. This report on model-based sensor placement describes a transformative two-tier approach to identify the optimum placement, number, and type of sensors for condition monitoring and fault diagnosis in fossil energy system operations. The algorithms are tested on a high fidelity model of the integrated gasification combined cycle (IGCC) plant. For a condition monitoring network, whether equipment should be considered at a unit level or a systems level depends upon the criticality of the process equipment, its likeliness to fail, and the level of resolution desired for any specific failure. Because of the presence of a high fidelity model at the unit level, a sensor network can be designed to monitor the spatial profile of the states and estimate fault severity levels. In an IGCC plant, besides the gasifier, the sour water gas shift (WGS) reactor plays an important role. In view of this, condition monitoring of the sour WGS reactor is considered at the unit level, while a detailed plant-wide model of gasification island, including sour WGS reactor and the Selexol process, is considered for fault diagnosis at the system-level. Finally, the developed algorithms unify the two levels and identifies an optimal sensor network that maximizes the effectiveness of the overall system-level fault diagnosis and component-level condition monitoring. This work could have a major impact on the design and operation of future fossil energy plants, particularly at the grassroots level where the sensor network is yet to be identified. In addition, the same algorithms developed in this report can be further enhanced to be used in retrofits, where the objectives could be upgrade (addition of more sensors) and relocation of existing sensors.

Diagnosis of mechanical pumping system using neural networks and system parameters analysis

International Nuclear Information System (INIS)

Tsai, Tai Ming; Wang, Wei Hui

2009-01-01

Normally, a mechanical pumping system is equipped to monitor some of the important input and output signals which are set to the prescribed values. This paper addressed dealing with these signals to establish the database of input- output relation by using a number of neural network models through learning algorithms. These signals encompass normal and abnormal running conditions. The abnormal running conditions were artificially generated. Meanwhile, for the purpose of setting up an on-line diagnosis network, the learning speed and accuracy of three kinds of networks, viz., the backpropagation (BPN), radial basis function (RBF) and adaptive linear (ADALINE) neural networks have been compared and assessed. The assessment criteria of the networks are compared with the correlation result matrix in terms of the neuron vectors. Both BPN and RBF are judged by the maximum vector based on the post-regression analysis, and the ADALINE is judged by the minimum vector based on the least mean square error analysis. By ignoring the neural network training time, it has been shown that if the mechanical diagnosis system is tackled off-line, the RBF method is suggested. However, for on-line diagnosis, the BPN method is recommended

Diagnosis of mechanical pumping system using neural networks and system parameters analysis

Energy Technology Data Exchange (ETDEWEB)

Tsai, Tai Ming; Wang, Wei Hui [National Taiwan Ocean University, Keelung (China)

2009-01-15

Normally, a mechanical pumping system is equipped to monitor some of the important input and output signals which are set to the prescribed values. This paper addressed dealing with these signals to establish the database of input- output relation by using a number of neural network models through learning algorithms. These signals encompass normal and abnormal running conditions. The abnormal running conditions were artificially generated. Meanwhile, for the purpose of setting up an on-line diagnosis network, the learning speed and accuracy of three kinds of networks, viz., the backpropagation (BPN), radial basis function (RBF) and adaptive linear (ADALINE) neural networks have been compared and assessed. The assessment criteria of the networks are compared with the correlation result matrix in terms of the neuron vectors. Both BPN and RBF are judged by the maximum vector based on the post-regression analysis, and the ADALINE is judged by the minimum vector based on the least mean square error analysis. By ignoring the neural network training time, it has been shown that if the mechanical diagnosis system is tackled off-line, the RBF method is suggested. However, for on-line diagnosis, the BPN method is recommended

Centrifugal compressor fault diagnosis based on qualitative simulation and thermal parameters

Science.gov (United States)

Lu, Yunsong; Wang, Fuli; Jia, Mingxing; Qi, Yuanchen

2016-12-01

This paper concerns fault diagnosis of centrifugal compressor based on thermal parameters. An improved qualitative simulation (QSIM) based fault diagnosis method is proposed to diagnose the faults of centrifugal compressor in a gas-steam combined-cycle power plant (CCPP). The qualitative models under normal and two faulty conditions have been built through the analysis of the principle of centrifugal compressor. To solve the problem of qualitative description of the observations of system variables, a qualitative trend extraction algorithm is applied to extract the trends of the observations. For qualitative states matching, a sliding window based matching strategy which consists of variables operating ranges constraints and qualitative constraints is proposed. The matching results are used to determine which QSIM model is more consistent with the running state of system. The correct diagnosis of two typical faults: seal leakage and valve stuck in the centrifugal compressor has validated the targeted performance of the proposed method, showing the advantages of fault roots containing in thermal parameters.

A handheld computer-aided diagnosis system and simulated analysis

Science.gov (United States)

Su, Mingjian; Zhang, Xuejun; Liu, Brent; Su, Kening; Louie, Ryan

2016-03-01

This paper describes a Computer Aided Diagnosis (CAD) system based on cellphone and distributed cluster. One of the bottlenecks in building a CAD system for clinical practice is the storage and process of mass pathology samples freely among different devices, and normal pattern matching algorithm on large scale image set is very time consuming. Distributed computation on cluster has demonstrated the ability to relieve this bottleneck. We develop a system enabling the user to compare the mass image to a dataset with feature table by sending datasets to Generic Data Handler Module in Hadoop, where the pattern recognition is undertaken for the detection of skin diseases. A single and combination retrieval algorithm to data pipeline base on Map Reduce framework is used in our system in order to make optimal choice between recognition accuracy and system cost. The profile of lesion area is drawn by doctors manually on the screen, and then uploads this pattern to the server. In our evaluation experiment, an accuracy of 75% diagnosis hit rate is obtained by testing 100 patients with skin illness. Our system has the potential help in building a novel medical image dataset by collecting large amounts of gold standard during medical diagnosis. Once the project is online, the participants are free to join and eventually an abundant sample dataset will soon be gathered enough for learning. These results demonstrate our technology is very promising and expected to be used in clinical practice.

Expert systems application to plant diagnosis and sensor data validation

International Nuclear Information System (INIS)

Hashemi, S.; Hajek, B.K.; Miller, D.W.; Chandrasekaran, B.; Josephson, J.R.

1986-01-01

In a nuclear power plant, over 2000 alarms and displays are available to the operator. For any given set of alarms and displays, the operator must be able to diagnose and correct the problem (s) quickly and accurately. At the same time, the operator is expected to distinguish the plant system faults from instrumentation channel failures and drifts. Needs for plant operator aids have been considered since the accident at TMI. Many of these aids are of the form of the Safety Parameter Display Systems and offer improved methods of displaying otherwise available data to the operator in a more concise and summarized format. diagnosis, however, remains a desirable objective of an operator aid. At The Ohio State University, faculty and students in nuclear engineering and computer science have evaluated this problem. The results of these studies have shown that plant diagnosis and sensor data validation must be considered as one integral problem and cannot be isolated from one another. Otherwise, an incorrect diagnosis based on faulty instrument information might be provided to the operator. In this study, the Knowlege Based System (KBS) technology is being incorporated to accomplish a final goal of an intelligent operator aid system

Method of modelization assistance with bond graphs and application to qualitative diagnosis of physical systems

International Nuclear Information System (INIS)

Lucas, B.

1994-05-01

After having recalled the usual diagnosis techniques (failure index, decision tree) and those based on an artificial intelligence approach, the author reports a research aimed at exploring the knowledge and model generation technique. He focuses on the design of an aid to model generation tool and aid-to-diagnosis tool. The bond graph technique is shown to be adapted to the aid to model generation, and is then adapted to the aid to diagnosis. The developed tool is applied to three projects: DIADEME (a diagnosis system based on physical model), the improvement of the SEXTANT diagnosis system (an expert system for transient analysis), and the investigation on an Ariane 5 launcher component. Notably, the author uses the Reiter and Greiner algorithm

A statistical-based approach for fault detection and diagnosis in a photovoltaic system

KAUST Repository

Garoudja, Elyes; Harrou, Fouzi; Sun, Ying; Kara, Kamel; Chouder, Aissa; Silvestre, Santiago

2017-01-01

This paper reports a development of a statistical approach for fault detection and diagnosis in a PV system. Specifically, the overarching goal of this work is to early detect and identify faults on the DC side of a PV system (e.g., short

A study on group decision-making based fault multi-symptom-domain consensus diagnosis

International Nuclear Information System (INIS)

He Yongyong; Chu Fulei; Zhong Binglin

2001-01-01

In the field of fault diagnosis for rotating machines, the conventional methods or the neural network based methods are mainly single symptom domain based methods, and the diagnosis accuracy of which is not always satisfactory. In this paper, in order to utilize multiple symptom domains to improve the diagnosis accuracy, an idea of fault multi-symptom-domain consensus diagnosis is developed. From the point of view of the group decision-making, two particular multi-symptom-domain diagnosis strategies are proposed. The proposed strategies use BP (Back-Propagation) neural networks as diagnosis models in various symptom domains, and then combine the outputs of these networks by two combination schemes, which are based on Dempster-Shafer evidence theory and fuzzy integral theory, respectively. Finally, a case study pertaining to the fault diagnosis for rotor-bearing systems is given in detail, and the results show that the proposed diagnosis strategies are feasible and more efficient than conventional stacked-vector methods

Data acquisition for a real time fault monitoring and diagnosis knowledge-based system for space power system

Science.gov (United States)

Wilhite, Larry D.; Lee, S. C.; Lollar, Louis F.

1989-01-01

The design and implementation of the real-time data acquisition and processing system employed in the AMPERES project is described, including effective data structures for efficient storage and flexible manipulation of the data by the knowledge-based system (KBS), the interprocess communication mechanism required between the data acquisition system and the KBS, and the appropriate data acquisition protocols for collecting data from the sensors. Sensor data are categorized as critical or noncritical data on the basis of the inherent frequencies of the signals and the diagnostic requirements reflected in their values. The critical data set contains 30 analog values and 42 digital values and is collected every 10 ms. The noncritical data set contains 240 analog values and is collected every second. The collected critical and noncritical data are stored in separate circular buffers. Buffers are created in shared memory to enable other processes, i.e., the fault monitoring and diagnosis process and the user interface process, to freely access the data sets.

Hospital payment systems based on diagnosis-related groups: experiences in low- and middle-income countries

Science.gov (United States)

Wittenbecher, Friedrich

2013-01-01

Abstract Objective This paper provides a comprehensive overview of hospital payment systems based on diagnosis-related groups (DRGs) in low- and middle-income countries. It also explores design and implementation issues and the related challenges countries face. Methods A literature research for papers on DRG-based payment systems in low- and middle-income countries was conducted in English, French and Spanish through Pubmed, the Pan American Health Organization’s Regional Library of Medicine and Google. Findings Twelve low- and middle-income countries have DRG-based payment systems and another 17 are in the piloting or exploratory stage. Countries have chosen from a wide range of imported and self-developed DRG models and most have adapted such models to their specific contexts. All countries have set expenditure ceilings. In general, systems were piloted before being implemented. The need to meet certain requirements in terms of coding standardization, data availability and information technology made implementation difficult. Private sector providers have not been fully integrated, but most countries have managed to delink hospital financing from public finance budgeting. Conclusion Although more evidence on the impact of DRG-based payment systems is needed, our findings suggest that (i) the greater portion of health-care financing should be public rather than private; (ii) it is advisable to pilot systems first and to establish expenditure ceilings; (iii) countries that import an existing variant of a DRG-based system should be mindful of the need for adaptation; and (iv) countries should promote the cooperation of providers for appropriate data generation and claims management. PMID:24115798

Web-based computer-aided-diagnosis (CAD) system for bone age assessment (BAA) of children

Science.gov (United States)

Zhang, Aifeng; Uyeda, Joshua; Tsao, Sinchai; Ma, Kevin; Vachon, Linda A.; Liu, Brent J.; Huang, H. K.

2008-03-01

Bone age assessment (BAA) of children is a clinical procedure frequently performed in pediatric radiology to evaluate the stage of skeletal maturation based on a left hand and wrist radiograph. The most commonly used standard: Greulich and Pyle (G&P) Hand Atlas was developed 50 years ago and exclusively based on Caucasian population. Moreover, inter- & intra-observer discrepancies using this method create a need of an objective and automatic BAA method. A digital hand atlas (DHA) has been collected with 1,400 hand images of normal children from Asian, African American, Caucasian and Hispanic descends. Based on DHA, a fully automatic, objective computer-aided-diagnosis (CAD) method was developed and it was adapted to specific population. To bring DHA and CAD method to the clinical environment as a useful tool in assisting radiologist to achieve higher accuracy in BAA, a web-based system with direct connection to a clinical site is designed as a novel clinical implementation approach for online and real time BAA. The core of the system, a CAD server receives the image from clinical site, processes it by the CAD method and finally, generates report. A web service publishes the results and radiologists at the clinical site can review it online within minutes. This prototype can be easily extended to multiple clinical sites and will provide the foundation for broader use of the CAD system for BAA.

Clinical diagnosis and typing of systemic amyloidosis in subcutaneous fat aspirates by mass spectrometry-based proteomics.

Science.gov (United States)

Vrana, Julie A; Theis, Jason D; Dasari, Surendra; Mereuta, Oana M; Dispenzieri, Angela; Zeldenrust, Steven R; Gertz, Morie A; Kurtin, Paul J; Grogg, Karen L; Dogan, Ahmet

2014-07-01

Examination of abdominal subcutaneous fat aspirates is a practical, sensitive and specific method for the diagnosis of systemic amyloidosis. Here we describe the development and implementation of a clinical assay using mass spectrometry-based proteomics to type amyloidosis in subcutaneous fat aspirates. First, we validated the assay comparing amyloid-positive (n=43) and -negative (n=26) subcutaneous fat aspirates. The assay classified amyloidosis with 88% sensitivity and 96% specificity. We then implemented the assay as a clinical test, and analyzed 366 amyloid-positive subcutaneous fat aspirates in a 4-year period as part of routine clinical care. The assay had a sensitivity of 90%, and diverse amyloid types, including immunoglobulin light chain (74%), transthyretin (13%), serum amyloid A (%1), gelsolin (1%), and lysozyme (1%), were identified. Using bioinformatics, we identified a universal amyloid proteome signature, which has high sensitivity and specificity for amyloidosis similar to that of Congo red staining. We curated proteome databases which included variant proteins associated with systemic amyloidosis, and identified clonotypic immunoglobulin variable gene usage in immunoglobulin light chain amyloidosis, and the variant peptides in hereditary transthyretin amyloidosis. In conclusion, mass spectrometry-based proteomic analysis of subcutaneous fat aspirates offers a powerful tool for the diagnosis and typing of systemic amyloidosis. The assay reveals the underlying pathogenesis by identifying variable gene usage in immunoglobulin light chains and the variant peptides in hereditary amyloidosis. Copyright© Ferrata Storti Foundation.

The Decision Support System in the Domain of Casting Defects Diagnosis

Directory of Open Access Journals (Sweden)

Wilk-Kołodziejczyk D.

2014-08-01

Full Text Available This article presents a computer system for the identification of casting defects using the methodology of Case-Based Reasoning. The system is a decision support tool in the diagnosis of defects in castings and is designed for small and medium-sized plants, where it is not possible to take advantage of multi-criteria data. Without access to complete process data, the diagnosis of casting defects requires the use of methods which process the information based on the experience and observations of a technologist responsible for the inspection of ready castings. The problem, known and studied for a long time, was decided to be solved with a computer system using a CBR (Case-Based Reasoning methodology. The CBR methodology not only allows using expert knowledge accumulated in the implementation phase, but also provides the system with an opportunity to “learn” by collecting new cases solved earlier by this system. The authors present a solution to the system of inference based on the accumulated cases, in which the main principle of operation is searching for similarities between the cases observed and cases stored in the knowledge base.

A Distributed and Collaborative Intelligent System for Medical Diagnosis

Directory of Open Access Journals (Sweden)

Wided LEJOUAD-CHAARI

2013-08-01

Full Text Available In this paper, we present a distributed collaborative system assisting physicians in diagnosis when processing medical images. This is a Web-based solution since the different participants and resources are on various sites. It is collaborative because these participants (physicians, radiologists, knowledgebasesdesigners, program developers for medical image processing, etc. can work collaboratively to enhance the quality of programs and then the quality of the diagnosis results. It is intelligent since it is a knowledge-based system including, but not only, a knowledge base, an inference engine said supervision engine and ontologies. The current work deals with the osteoporosis detection in bone radiographies. We rely on program supervision techniques that aim to automatically plan and control complex software usage. Our main contribution is to allow physicians, who are not experts in computing, to benefit from technological advances made by experts in image processing, and then to efficiently use various osteoporosis detection programs in a distributed environment.

Design of a real-time fault diagnosis expert system for the EAST cryoplant

International Nuclear Information System (INIS)

Zhou Zhiwei; Zhuang Ming; Lu Xiaofei; Hu Liangbing; Xia Genhai

2012-01-01

Highlights: ► An expert system of real-time fault diagnosis for EAST cryoplant is designed. ► Knowledge base is built via fault tree analysis based on our fault experience. ► It can make up the deficiency of safety monitoring in cryogenic DCS. ► It can help operators to find the fault causes and give operation suggestion. ► It plays a role of operators training in certain degree. - Abstract: The EAST cryoplant consists of a 2 kW/4 K helium refrigerator and a helium distribution system. It is a complex process system which involves many process variables and cryogenic equipments. Each potential fault or abnormal event may influence stability and safety of the cryogenic system, thereby disturbing the fusion experiment. The cryogenic control system can monitor the process data and detect process alarms, but it is difficult to effectively diagnose the fault causes and provide operation suggestions to operators when anomalies occur. Therefore, a real-time fault diagnosis expert system is essential for a safe and steady operation of EAST cryogenic system. After a brief description of the EAST cryoplant and its control system, the structure design of the cryogenic fault diagnosis expert system is proposed. Based on the empirical knowledge, the fault diagnosis model is built adopting fault tree analysis method which considers the uncertainty. The knowledge base and the inference machine are presented in detail. A cross-platform integrated development environment Qt Creator and MySQL database have been used to develop the system. The proposed expert system has a fine graphic user interface for monitoring and operation. Preliminary test was conducted and the results found to be satisfactory.

Building and application of the performance diagnosis system for nuclear power plants

International Nuclear Information System (INIS)

Ono, S.; Kanbara, K.; Sugawara, Y.

2010-01-01

To achieve a low-carbon society, we promote utilization of nuclear energy, which plays a zero-emission power generation. Therefore the nuclear power plants have been imposed a stable supply of electricity. The condition based maintenance (CBM) is effective in order to maintain a stable operation of the nuclear power plants. We built the performance diagnosis system based on the heat and mass balance calculation as one of supporting tools for the CBM. Moreover we note that the performance diagnosis system is built for steam turbine cycle operating with saturated steam conditions. (author)

Energy systems Diagnosis in developing countries

International Nuclear Information System (INIS)

Girod, J.

1991-01-01

Energy systems diagnosis is necessary to allow evaluation of energy balance by administration and political authorities of a country. First, the author describes the principle stages of energetic diagnosis. Then this work is divided into three parts: First part: Energy consumption diagnosis in several districts (families, utilities, agriculture, transport, industry) Second part: Energy supplies diagnosis (energy markets). Third part: Interactions between energy consumption and energy supply. 28 figs.; 52 tabs.; 107 refs

A qualitative diagnosis method for a continuous process monitor system

International Nuclear Information System (INIS)

Lucas, B.; Evrard, J.M.; Lorre, J.P.

1993-01-01

SEXTANT, an expert system for the analysis of transients, was built initially to study physical transients in nuclear reactors. It combines several knowledge bases concerning measurements, models and qualitative behavior of the plant with a generate-and-test mechanism and a set of numerical models of the physical process. The integration of an improved diagnosis method using a mixed model in SEXTANT in order to take into account the existence and the reliability of only a few number of sensors, the knowledge on failure and the possibility of non anticipated failures, is presented. This diagnosis method is based on two complementary qualitative models of the process and a methodology to build these models from a system description. 8 figs., 17 refs

A Fault Diagnosis Approach for the Hydraulic System by Artificial Neural Networks

OpenAIRE

Xiangyu He; Shanghong He

2014-01-01

Based on artificial neural networks, a fault diagnosis approach for the hydraulic system was proposed in this paper. Normal state samples were used as the training data to develop a dynamic general regression neural network (DGRNN) model. The trained DGRNN model then served as the fault determinant to diagnose test faults and the work condition of the hydraulic system was identified. Several typical faults of the hydraulic system were used to verify the fault diagnosis approach. Experiment re...

Learning and case-based reasoning for faults diagnosis-aiding in nuclear power plants

International Nuclear Information System (INIS)

Nicolini, C.

1998-01-01

The aim of this thesis is the design of a faults diagnosis-aiding system in a nuclear facility of the Cea. Actually the existing system allows the optimization of the production processes in regular operating conditions. Meanwhile during accidental events, the alarms, managed by threshold, are bringing no relevant information. To increase the reliability and the safety, the human operator needs a faults diagnosis-aiding system. The developed system, SECAPI, combines problem solving techniques and automatic learning techniques, that allow the diagnosis and the the simulation of various faults happening on nuclear facilities. Its reasoning principle uses case-based and rules-based techniques. SECAPI owns a learning module which reads out knowledge connected with faults. It can then simulate various faults, using the inductive logical computing. SECAPI has been applied on a radioactive tritium treatment operating channel, at the Cea with good results. (A.L.B.)

Breath analysis based on micropreconcentrator for early cancer diagnosis

Science.gov (United States)

Lee, Sang-Seok

2018-02-01

We are developing micropreconcentrators based on micro/nanotechnology to detect trace levels of volatile organic compound (VOC) gases contained in human and canine exhaled breath. The possibility of using exhaled VOC gases as biomarkers for various cancer diagnoses has been previously discussed. For early cancer diagnosis, detection of trace levels of VOC gas is indispensable. Using micropreconcentrators based on MEMS technology or nanotechnology is very promising for detection of VOC gas. A micropreconcentrator based breath analysis technique also has advantages from the viewpoints of cost performance and availability for various cancers diagnosis. In this paper, we introduce design, fabrication and evaluation results of our MEMS and nanotechnology based micropreconcentrators. In the MEMS based device, we propose a flower leaf type Si microstructure, and its shape and configuration are optimized quantitatively by finite element method simulation. The nanotechnology based micropreconcentrator consists of carbon nanotube (CNT) structures. As a result, we achieve ppb level VOC gas detection with our micropreconcentrators and usual gas chromatography system that can detect on the order of ppm VOC in gas samples. In performance evaluation, we also confirm that the CNT based micropreconcentrator shows 115 times better concentration ratio than that of the Si based micropreconcentrator. Moreover, we discuss a commercialization idea for new cancer diagnosis using breath analysis. Future work and preliminary clinical testing in dogs is also discussed.

Design of a real-time fault diagnosis expert system for the EAST cryoplant

Energy Technology Data Exchange (ETDEWEB)

Zhou Zhiwei, E-mail: zzw@ipp.ac.cn [Institute of Plasma Physics, Chinese Academy of Sciences, Hefei, Anhui (China); Zhuang Ming, E-mail: zhm@ipp.ac.cn [Institute of Plasma Physics, Chinese Academy of Sciences, Hefei, Anhui (China); Lu Xiaofei, E-mail: luxf1212@mail.ustc.edu.cn [Institute of Plasma Physics, Chinese Academy of Sciences, Hefei, Anhui (China); Hu Liangbing, E-mail: huliangbing@ipp.ac.cn [Institute of Plasma Physics, Chinese Academy of Sciences, Hefei, Anhui (China); Xia Genhai, E-mail: xgh@ipp.ac.cn [Institute of Plasma Physics, Chinese Academy of Sciences, Hefei, Anhui (China)

2012-12-15

Highlights: Black-Right-Pointing-Pointer An expert system of real-time fault diagnosis for EAST cryoplant is designed. Black-Right-Pointing-Pointer Knowledge base is built via fault tree analysis based on our fault experience. Black-Right-Pointing-Pointer It can make up the deficiency of safety monitoring in cryogenic DCS. Black-Right-Pointing-Pointer It can help operators to find the fault causes and give operation suggestion. Black-Right-Pointing-Pointer It plays a role of operators training in certain degree. - Abstract: The EAST cryoplant consists of a 2 kW/4 K helium refrigerator and a helium distribution system. It is a complex process system which involves many process variables and cryogenic equipments. Each potential fault or abnormal event may influence stability and safety of the cryogenic system, thereby disturbing the fusion experiment. The cryogenic control system can monitor the process data and detect process alarms, but it is difficult to effectively diagnose the fault causes and provide operation suggestions to operators when anomalies occur. Therefore, a real-time fault diagnosis expert system is essential for a safe and steady operation of EAST cryogenic system. After a brief description of the EAST cryoplant and its control system, the structure design of the cryogenic fault diagnosis expert system is proposed. Based on the empirical knowledge, the fault diagnosis model is built adopting fault tree analysis method which considers the uncertainty. The knowledge base and the inference machine are presented in detail. A cross-platform integrated development environment Qt Creator and MySQL database have been used to develop the system. The proposed expert system has a fine graphic user interface for monitoring and operation. Preliminary test was conducted and the results found to be satisfactory.

Intelligent System for Diagnosis of a Three-Phase Separator

Directory of Open Access Journals (Sweden)

Irina Ioniţă

2016-03-01

Full Text Available Intelligent systems for diagnosis have been used in a variety of domains: financial evaluation, credit scoring problem, identification of software and hardware problems of mechanical and electronic equipment, medical diagnosis, fault detection in gas-oil production plants etc. The goal of diagnosis systems is to classify the observed symptoms as being caused by some diagnosis class while advising systems perform such a classification and offer corrective remedies (recommendations. The current paper discuss the opportunity to combine more intelligent techniques and methodologies (intelligent agents, data mining and expert systems to increase the accuracy of results obtained from the diagnosis of a three-phase separator. The results indicate that the diagnosis hybrid system benefits from the advantages of each module component: intelligent agent module, data mining module and expert system module.

Experience on a BWR plant diagnosis system

International Nuclear Information System (INIS)

Tanabe, A.; Kawai, K.; Hashimoto, Y.

1981-01-01

It is important to watch plant dynamics and equipment condition for avoiding a big transient or avoiding damage to a system by equipment failure. After the TMI accident the necessity of a diagnosis system has been recognized and such development activities have become of primary importance in many organizations. A diagnosis system has two kinds of function. One is the early detection of an anomaly before detection by a conventional instrumentation system. The other is appropriate instruction after alarm or scram has occurred. The authors have been developing the former system by a noise analysis technique and a feasibility study has been undertaken in recent years as a joint research programme of several electric power companies and the Toshiba Corporation. A prototype diagnosis system has been installed on a BWR plant in Japan. This diagnosis system concerns reactor core, jet pumps and three main control systems. Many data from normal operation have been accumulated using this system and a variation pattern of normal noise data is clarified. On this basis, anomally detection criteria have been determined using statistical decision theory. It is confirmed that this system performance is satisfactory, and that the system will be of great use for surveillance of core and control systems without artificial disturbances. (author)

An in-depth assessment of a diagnosis-based risk adjustment model based on national health insurance claims: the application of the Johns Hopkins Adjusted Clinical Group case-mix system in Taiwan.

Science.gov (United States)

Chang, Hsien-Yen; Weiner, Jonathan P

2010-01-18

Diagnosis-based risk adjustment is becoming an important issue globally as a result of its implications for payment, high-risk predictive modelling and provider performance assessment. The Taiwanese National Health Insurance (NHI) programme provides universal coverage and maintains a single national computerized claims database, which enables the application of diagnosis-based risk adjustment. However, research regarding risk adjustment is limited. This study aims to examine the performance of the Adjusted Clinical Group (ACG) case-mix system using claims-based diagnosis information from the Taiwanese NHI programme. A random sample of NHI enrollees was selected. Those continuously enrolled in 2002 were included for concurrent analyses (n = 173,234), while those in both 2002 and 2003 were included for prospective analyses (n = 164,562). Health status measures derived from 2002 diagnoses were used to explain the 2002 and 2003 health expenditure. A multivariate linear regression model was adopted after comparing the performance of seven different statistical models. Split-validation was performed in order to avoid overfitting. The performance measures were adjusted R2 and mean absolute prediction error of five types of expenditure at individual level, and predictive ratio of total expenditure at group level. The more comprehensive models performed better when used for explaining resource utilization. Adjusted R2 of total expenditure in concurrent/prospective analyses were 4.2%/4.4% in the demographic model, 15%/10% in the ACGs or ADGs (Aggregated Diagnosis Group) model, and 40%/22% in the models containing EDCs (Expanded Diagnosis Cluster). When predicting expenditure for groups based on expenditure quintiles, all models underpredicted the highest expenditure group and overpredicted the four other groups. For groups based on morbidity burden, the ACGs model had the best performance overall. Given the widespread availability of claims data and the superior explanatory

CIMIDx: Prototype for a Cloud-Based System to Support Intelligent Medical Image Diagnosis With Efficiency.

Science.gov (United States)

Bhavani, Selvaraj Rani; Senthilkumar, Jagatheesan; Chilambuchelvan, Arul Gnanaprakasam; Manjula, Dhanabalachandran; Krishnamoorthy, Ramasamy; Kannan, Arputharaj

2015-03-27

The Internet has greatly enhanced health care, helping patients stay up-to-date on medical issues and general knowledge. Many cancer patients use the Internet for cancer diagnosis and related information. Recently, cloud computing has emerged as a new way of delivering health services but currently, there is no generic and fully automated cloud-based self-management intervention for breast cancer patients, as practical guidelines are lacking. We investigated the prevalence and predictors of cloud use for medical diagnosis among women with breast cancer to gain insight into meaningful usage parameters to evaluate the use of generic, fully automated cloud-based self-intervention, by assessing how breast cancer survivors use a generic self-management model. The goal of this study was implemented and evaluated with a new prototype called "CIMIDx", based on representative association rules that support the diagnosis of medical images (mammograms). The proposed Cloud-Based System Support Intelligent Medical Image Diagnosis (CIMIDx) prototype includes two modules. The first is the design and development of the CIMIDx training and test cloud services. Deployed in the cloud, the prototype can be used for diagnosis and screening mammography by assessing the cancers detected, tumor sizes, histology, and stage of classification accuracy. To analyze the prototype's classification accuracy, we conducted an experiment with data provided by clients. Second, by monitoring cloud server requests, the CIMIDx usage statistics were recorded for the cloud-based self-intervention groups. We conducted an evaluation of the CIMIDx cloud service usage, in which browsing functionalities were evaluated from the end-user's perspective. We performed several experiments to validate the CIMIDx prototype for breast health issues. The first set of experiments evaluated the diagnostic performance of the CIMIDx framework. We collected medical information from 150 breast cancer survivors from hospitals

Incipient multiple fault diagnosis in real time with applications to large-scale systems

International Nuclear Information System (INIS)

Chung, H.Y.; Bien, Z.; Park, J.H.; Seon, P.H.

1994-01-01

By using a modified signed directed graph (SDG) together with the distributed artificial neutral networks and a knowledge-based system, a method of incipient multi-fault diagnosis is presented for large-scale physical systems with complex pipes and instrumentations such as valves, actuators, sensors, and controllers. The proposed method is designed so as to (1) make a real-time incipient fault diagnosis possible for large-scale systems, (2) perform the fault diagnosis not only in the steady-state case but also in the transient case as well by using a concept of fault propagation time, which is newly adopted in the SDG model, (3) provide with highly reliable diagnosis results and explanation capability of faults diagnosed as in an expert system, and (4) diagnose the pipe damage such as leaking, break, or throttling. This method is applied for diagnosis of a pressurizer in the Kori Nuclear Power Plant (NPP) unit 2 in Korea under a transient condition, and its result is reported to show satisfactory performance of the method for the incipient multi-fault diagnosis of such a large-scale system in a real-time manner

An Approach of Diagnosis Based On The Hidden Markov Chains Model

Directory of Open Access Journals (Sweden)

Karim Bouamrane

2008-07-01

Full Text Available Diagnosis is a key element in industrial system maintenance process performance. A diagnosis tool is proposed allowing the maintenance operators capitalizing on the knowledge of their trade and subdividing it for better performance improvement and intervention effectiveness within the maintenance process service. The Tool is based on the Markov Chain Model and more precisely the Hidden Markov Chains (HMC which has the system failures determination advantage, taking into account the causal relations, stochastic context modeling of their dynamics and providing a relevant diagnosis help by their ability of dubious information use. Since the FMEA method is a well adapted artificial intelligence field, the modeling with Markov Chains is carried out with its assistance. Recently, a dynamic programming recursive algorithm, called 'Viterbi Algorithm', is being used in the Hidden Markov Chains field. This algorithm provides as input to the HMC a set of system observed effects and generates at exit the various causes having caused the loss from one or several system functions.

Fault diagnosis method based on FFT-RPCA-SVM for Cascaded-Multilevel Inverter.

Science.gov (United States)

Wang, Tianzhen; Qi, Jie; Xu, Hao; Wang, Yide; Liu, Lei; Gao, Diju

2016-01-01

Thanks to reduced switch stress, high quality of load wave, easy packaging and good extensibility, the cascaded H-bridge multilevel inverter is widely used in wind power system. To guarantee stable operation of system, a new fault diagnosis method, based on Fast Fourier Transform (FFT), Relative Principle Component Analysis (RPCA) and Support Vector Machine (SVM), is proposed for H-bridge multilevel inverter. To avoid the influence of load variation on fault diagnosis, the output voltages of the inverter is chosen as the fault characteristic signals. To shorten the time of diagnosis and improve the diagnostic accuracy, the main features of the fault characteristic signals are extracted by FFT. To further reduce the training time of SVM, the feature vector is reduced based on RPCA that can get a lower dimensional feature space. The fault classifier is constructed via SVM. An experimental prototype of the inverter is built to test the proposed method. Compared to other fault diagnosis methods, the experimental results demonstrate the high accuracy and efficiency of the proposed method. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Reliable fault detection and diagnosis of photovoltaic systems based on statistical monitoring approaches

KAUST Repository

Harrou, Fouzi; Sun, Ying; Taghezouit, Bilal; Saidi, Ahmed; Hamlati, Mohamed-Elkarim

2017-01-01

This study reports the development of an innovative fault detection and diagnosis scheme to monitor the direct current (DC) side of photovoltaic (PV) systems. Towards this end, we propose a statistical approach that exploits the advantages of one

Ontology-Based Method for Fault Diagnosis of Loaders.

Science.gov (United States)

Xu, Feixiang; Liu, Xinhui; Chen, Wei; Zhou, Chen; Cao, Bingwei

2018-02-28

This paper proposes an ontology-based fault diagnosis method which overcomes the difficulty of understanding complex fault diagnosis knowledge of loaders and offers a universal approach for fault diagnosis of all loaders. This method contains the following components: (1) An ontology-based fault diagnosis model is proposed to achieve the integrating, sharing and reusing of fault diagnosis knowledge for loaders; (2) combined with ontology, CBR (case-based reasoning) is introduced to realize effective and accurate fault diagnoses following four steps (feature selection, case-retrieval, case-matching and case-updating); and (3) in order to cover the shortages of the CBR method due to the lack of concerned cases, ontology based RBR (rule-based reasoning) is put forward through building SWRL (Semantic Web Rule Language) rules. An application program is also developed to implement the above methods to assist in finding the fault causes, fault locations and maintenance measures of loaders. In addition, the program is validated through analyzing a case study.

Decision Support System In Heart Disease Diagnosis By Case Based Recommendation

Directory of Open Access Journals (Sweden)

Prinsha Prakash

2015-02-01

Full Text Available Abstract Heart disease is the main leading killer as well as a major cause of disability. Its timely detection and correct diagnosis plays a vital role in human life. In a limited period of time recalling the data from Doctors unaided memory may lead to wrong judgments. While taking decisions Doctor analyses the physical condition and test results of the patient. In the same way our system compares the data provided to Doctor and getting a result through CBR technique. Results from the system will help the Doctor to conclude the decision and reduce human errors may occur. Our system is able to analyze scanned results of heart and being a helping hand to the doctor in all manners.

A study on diagnosis of Dysmenorrhea patients by Diagnosis System of Oriental Medicine

Directory of Open Access Journals (Sweden)

In Sun,Lee

2007-02-01

Full Text Available Purpose : This study was undertaken to make a diagnosis weakness and firmness (虛實 of Dysmenorrhea patients by diagnosis questionnaires system(Diagnosis System of Oriental Medicine-DSOM Methods : The subjects were 58 volunteers who was suffering for dysmenorrhea, employed using Measure of Menstrual Pain (MMP questionnaire. The had agreed to take part in this experiment, with didn't take any anodyne drugs. The MMP score by using 7 questions and the Menstrual Symptom Severity List(MSSL-D was measured before and after menstruation cycle. Results and Conclusions : The findings of this study were as follows; 1. We examined Pathogenic Factor's frequency of DSOM, Coldness(寒 was 45 persons 80.36%, Damp(濕 was 40 persons 71.43%, Heart(心 was 37 persons 66.07%, Heat syndrom(熱 was 9 persons 16.07%, insufficiency of Yang(陽虛 was 6 persons 10.71%. 2. We divided Dysmenorrhea patients into two groups(weakness and firmness by Results of DSOM, Firmness was 25 Persons 43.1%, Weakness was 23 persons 39.7%, Unknown was 10 persons 17.2%. 3. In estimation based on Measure of Menstrual Pain (MMP questionnaire Severe menstrual pain is weakness, Mild menstrual pain is Firmness. 4. In estimation of coldness and heat syndrom, Coldness was 40 persons 69.0%, Heat syndrom, was 2 persons 3.5%, Possess both coldness and heat syndrom was 9 persons 15.5%.

Development of water chemistry diagnosis system for BWR primary loop

International Nuclear Information System (INIS)

Nagase, Makoto; Asakura, Yamato; Sakagami, Masaharu; Uchida, Shunsuke; Ohsumi, Katsumi.

1988-01-01

The prototype of a water chemistry diagnosis system for BWR primary loop has been developed. Its purposes are improvement of water chemistry control and reduction of the work burden on plant chemistry personnel. It has three main features as follows. (1) Intensifying the observation of water chemistry conditions by variable sampling intervals based on the on-line measured data. (2) Early detection of water chemistry data trends using a second order regression curve which is calculated from the measured data, and then searching the cause of anomaly if anything (3) Diagnosis of Fe concentration in feedwater using model simulations, in order to lower the radiation level in the primary system. (author)

Porous TiO₂-Based Gas Sensors for Cyber Chemical Systems to Provide Security and Medical Diagnosis.

Science.gov (United States)

Galstyan, Vardan

2017-12-19

Gas sensors play an important role in our life, providing control and security of technical processes, environment, transportation and healthcare. Consequently, the development of high performance gas sensor devices is the subject of intense research. TiO₂, with its excellent physical and chemical properties, is a very attractive material for the fabrication of chemical sensors. Meanwhile, the emerging technologies are focused on the fabrication of more flexible and smart systems for precise monitoring and diagnosis in real-time. The proposed cyber chemical systems in this paper are based on the integration of cyber elements with the chemical sensor devices. These systems may have a crucial effect on the environmental and industrial safety, control of carriage of dangerous goods and medicine. This review highlights the recent developments on fabrication of porous TiO₂-based chemical gas sensors for their application in cyber chemical system showing the convenience and feasibility of such a model to provide the security and to perform the diagnostics. The most of reports have demonstrated that the fabrication of doped, mixed and composite structures based on porous TiO₂ may drastically improve its sensing performance. In addition, each component has its unique effect on the sensing properties of material.

Design for testability and diagnosis at the system-level

Science.gov (United States)

Simpson, William R.; Sheppard, John W.

1993-01-01

The growing complexity of full-scale systems has surpassed the capabilities of most simulation software to provide detailed models or gate-level failure analyses. The process of system-level diagnosis approaches the fault-isolation problem in a manner that differs significantly from the traditional and exhaustive failure mode search. System-level diagnosis is based on a functional representation of the system. For example, one can exercise one portion of a radar algorithm (the Fast Fourier Transform (FFT) function) by injecting several standard input patterns and comparing the results to standardized output results. An anomalous output would point to one of several items (including the FFT circuit) without specifying the gate or failure mode. For system-level repair, identifying an anomalous chip is sufficient. We describe here an information theoretic and dependency modeling approach that discards much of the detailed physical knowledge about the system and analyzes its information flow and functional interrelationships. The approach relies on group and flow associations and, as such, is hierarchical. Its hierarchical nature allows the approach to be applicable to any level of complexity and to any repair level. This approach has been incorporated in a product called STAMP (System Testability and Maintenance Program) which was developed and refined through more than 10 years of field-level applications to complex system diagnosis. The results have been outstanding, even spectacular in some cases. In this paper we describe system-level testability, system-level diagnoses, and the STAMP analysis approach, as well as a few STAMP applications.

Review of Diagnosis-Related Group-Based Financing of Hospital Care

Directory of Open Access Journals (Sweden)

Natasa Mihailovic

2016-05-01

Full Text Available Since the 1990s, diagnosis-related group (DRG-based payment systems were gradually introduced in many countries. The main design characteristics of a DRG-based payment system are an exhaustive patient case classification system (ie, the system of diagnosis-related groupings and the payment formula, which is based on the base rate multiplied by a relative cost weight specific for each DRG. Cases within the same DRG code group are expected to undergo similar clinical evolution. Consecutively, they should incur the costs of diagnostics and treatment within a predefined scale. Such predictability was proven in a number of cost-of-illness studies conducted on major prosperity diseases alongside clinical trials on efficiency. This was the case with risky pregnancies, chronic obstructive pulmonary disease, diabetes, depression, alcohol addiction, hepatitis, and cancer. This article presents experience of introduced DRG-based payments in countries of western and eastern Europe, Scandinavia, United States, Canada, and Australia. This article presents the results of few selected reviews and systematic reviews of the following evidence: published reports on health system reforms by World Health Organization, World Bank, Organization for Economic Co-operation and Development, Canadian Institute for Health Information, Canadian Health Services Research Foundation, and Centre for Health Economics University of York. Diverse payment systems have different strengths and weaknesses in relation to the various objectives. The advantages of the DRG payment system are reflected in the increased efficiency and transparency and reduced average length of stay. The disadvantage of DRG is creating financial incentives toward earlier hospital discharges. Occasionally, such polices are not in full accordance with the clinical benefit priorities.

Hydra: A web-based system for cardiovascular analysis, diagnosis and treatment.

Science.gov (United States)

Novo, J; Hermida, A; Ortega, M; Barreira, N; Penedo, M G; López, J E; Calvo, C

2017-02-01

Cardiovascular (CV) risk stratification is a highly complex process involving an extensive set of clinical trials to support the clinical decision-making process. There are many clinical conditions (e.g. diabetes, obesity, stress, etc.) that can lead to the early diagnosis or establishment of cardiovascular disease. In order to determine all these clinical conditions, a complete set of clinical patient analyses is typically performed, including a physical examination, blood analysis, electrocardiogram, blood pressure (BP) analysis, etc. This article presents a web-based system, called Hydra, which integrates a full and detailed set of services and functionalities for clinical decision support in order to help and improve the work of clinicians in cardiovascular patient diagnosis, risk assessment, treatment and monitoring over time. Hydra integrates a number of different services: a service for inputting all the information gathered by specialists (physical examination, habits, BP, blood analysis, electrocardiogram, etc.); a tool to automatically determine the CV risk stratification, including well-known standard risk stratification tables; and, finally, various tools to incorporate, analyze and graphically present the records of the ambulatory BP monitoring that provides BP analysis over a given period of time (24 or 48 hours). In addition, the platform presents a set of reports derived from all the information gathered from the patient in order to support physicians in their clinical decisions. Hydra was tested and validated in a real domain. In particular, internal medicine specialists at the Hypertension Unit of the Santiago de Compostela University Hospital (CHUS) validated the platform and used it in different clinical studies to demonstrate its utility. It was observed that the platform increased productivity and accuracy in the assessment of patient data yielding a cost reduction in clinical practice. This paper proposes a complete platform that includes

Fault diagnosis and refrigerant leak detection in vapour compression refrigeration systems

Energy Technology Data Exchange (ETDEWEB)

Tassou, S.A.; Grace, I.N. [Brunel University, Uxbridge (United Kingdom). Department of Mechanical Engineering

2005-08-01

The environmental impact of refrigeration systems can be reduced by operation at higher efficiency and reduction of refrigerant leakage. Refrigerant loss contributes both directly and indirectly to global warming through inefficient system operation, increased power consumption and greenhouse gas emissions and higher maintenance costs. Existing sensor-based leak detection methods are limited by the inability to detect gradual leakage and the need for careful sensor location. There is a requirement for a real-time performance monitoring approach to leak detection and fault diagnosis which overcomes these disadvantages. This paper reports on the development of a fault diagnosis and refrigerant leak detection system based on artificial intelligence and real-time performance monitoring. The system has been used successfully to distinguish between faulty and fault free operation, steady-state and transient operation, leakage and over charge conditions. Work currently underway is aimed at testing additional fault conditions and establishing further rules to distinguish between these patterns. (author)

An in-depth assessment of a diagnosis-based risk adjustment model based on national health insurance claims: the application of the Johns Hopkins Adjusted Clinical Group case-mix system in Taiwan

Directory of Open Access Journals (Sweden)

Weiner Jonathan P

2010-01-01

Full Text Available Abstract Background Diagnosis-based risk adjustment is becoming an important issue globally as a result of its implications for payment, high-risk predictive modelling and provider performance assessment. The Taiwanese National Health Insurance (NHI programme provides universal coverage and maintains a single national computerized claims database, which enables the application of diagnosis-based risk adjustment. However, research regarding risk adjustment is limited. This study aims to examine the performance of the Adjusted Clinical Group (ACG case-mix system using claims-based diagnosis information from the Taiwanese NHI programme. Methods A random sample of NHI enrollees was selected. Those continuously enrolled in 2002 were included for concurrent analyses (n = 173,234, while those in both 2002 and 2003 were included for prospective analyses (n = 164,562. Health status measures derived from 2002 diagnoses were used to explain the 2002 and 2003 health expenditure. A multivariate linear regression model was adopted after comparing the performance of seven different statistical models. Split-validation was performed in order to avoid overfitting. The performance measures were adjusted R2 and mean absolute prediction error of five types of expenditure at individual level, and predictive ratio of total expenditure at group level. Results The more comprehensive models performed better when used for explaining resource utilization. Adjusted R2 of total expenditure in concurrent/prospective analyses were 4.2%/4.4% in the demographic model, 15%/10% in the ACGs or ADGs (Aggregated Diagnosis Group model, and 40%/22% in the models containing EDCs (Expanded Diagnosis Cluster. When predicting expenditure for groups based on expenditure quintiles, all models underpredicted the highest expenditure group and overpredicted the four other groups. For groups based on morbidity burden, the ACGs model had the best performance overall. Conclusions Given the

Fault Diagnosis of Power System Based on Improved Genetic Optimized BP-NN

Directory of Open Access Journals (Sweden)

Yuan Pu

2015-01-01

Full Text Available BP neural network (Back-Propagation Neural Network, BP-NN is one of the most widely neural network models and is applied to fault diagnosis of power system currently. BP neural network has good self-learning and adaptive ability and generalization ability, but the operation process is easy to fall into local minima. Genetic algorithm has global optimization features, and crossover is the most important operation of the Genetic Algorithm. In this paper, we can modify the crossover of traditional Genetic Algorithm, using improved genetic algorithm optimized BP neural network training initial weights and thresholds, to avoid the problem of BP neural network fall into local minima. The results of analysis by an example, the method can efficiently diagnose network fault location, and improve fault-tolerance and grid fault diagnosis effect.

Advanced neural network-based computational schemes for robust fault diagnosis

CERN Document Server

Mrugalski, Marcin

2014-01-01

The present book is devoted to problems of adaptation of artificial neural networks to robust fault diagnosis schemes. It presents neural networks-based modelling and estimation techniques used for designing robust fault diagnosis schemes for non-linear dynamic systems. A part of the book focuses on fundamental issues such as architectures of dynamic neural networks, methods for designing of neural networks and fault diagnosis schemes as well as the importance of robustness. The book is of a tutorial value and can be perceived as a good starting point for the new-comers to this field. The book is also devoted to advanced schemes of description of neural model uncertainty. In particular, the methods of computation of neural networks uncertainty with robust parameter estimation are presented. Moreover, a novel approach for system identification with the state-space GMDH neural network is delivered. All the concepts described in this book are illustrated by both simple academic illustrative examples and practica...

Naive Bayes Bearing Fault Diagnosis Based on Enhanced Independence of Data.

Science.gov (United States)

Zhang, Nannan; Wu, Lifeng; Yang, Jing; Guan, Yong

2018-02-05

The bearing is the key component of rotating machinery, and its performance directly determines the reliability and safety of the system. Data-based bearing fault diagnosis has become a research hotspot. Naive Bayes (NB), which is based on independent presumption, is widely used in fault diagnosis. However, the bearing data are not completely independent, which reduces the performance of NB algorithms. In order to solve this problem, we propose a NB bearing fault diagnosis method based on enhanced independence of data. The method deals with data vector from two aspects: the attribute feature and the sample dimension. After processing, the classification limitation of NB is reduced by the independence hypothesis. First, we extract the statistical characteristics of the original signal of the bearings effectively. Then, the Decision Tree algorithm is used to select the important features of the time domain signal, and the low correlation features is selected. Next, the Selective Support Vector Machine (SSVM) is used to prune the dimension data and remove redundant vectors. Finally, we use NB to diagnose the fault with the low correlation data. The experimental results show that the independent enhancement of data is effective for bearing fault diagnosis.

Naive Bayes Bearing Fault Diagnosis Based on Enhanced Independence of Data

Science.gov (United States)

Zhang, Nannan; Wu, Lifeng; Yang, Jing; Guan, Yong

2018-01-01

The bearing is the key component of rotating machinery, and its performance directly determines the reliability and safety of the system. Data-based bearing fault diagnosis has become a research hotspot. Naive Bayes (NB), which is based on independent presumption, is widely used in fault diagnosis. However, the bearing data are not completely independent, which reduces the performance of NB algorithms. In order to solve this problem, we propose a NB bearing fault diagnosis method based on enhanced independence of data. The method deals with data vector from two aspects: the attribute feature and the sample dimension. After processing, the classification limitation of NB is reduced by the independence hypothesis. First, we extract the statistical characteristics of the original signal of the bearings effectively. Then, the Decision Tree algorithm is used to select the important features of the time domain signal, and the low correlation features is selected. Next, the Selective Support Vector Machine (SSVM) is used to prune the dimension data and remove redundant vectors. Finally, we use NB to diagnose the fault with the low correlation data. The experimental results show that the independent enhancement of data is effective for bearing fault diagnosis. PMID:29401730

Expert systems for real-time monitoring and fault diagnosis

Science.gov (United States)

Edwards, S. J.; Caglayan, A. K.

1989-01-01

Methods for building real-time onboard expert systems were investigated, and the use of expert systems technology was demonstrated in improving the performance of current real-time onboard monitoring and fault diagnosis applications. The potential applications of the proposed research include an expert system environment allowing the integration of expert systems into conventional time-critical application solutions, a grammar for describing the discrete event behavior of monitoring and fault diagnosis systems, and their applications to new real-time hardware fault diagnosis and monitoring systems for aircraft.

Radiologic diagnosis of bone tumours using Webonex, a web-based artificial intelligence program

International Nuclear Information System (INIS)

Rasuli, P.; Rasouli, F.; Rasouli, T.

2001-01-01

Knowledge-based system is a decision support system in which an expert's knowledge and reasoning can be applied to problems in bounded knowledge domains. These systems, using knowledge and inference techniques, mimic human reasoning to solve problems. Knowledge-based systems are said to be 'intelligent' because they possess massive stores of information and exhibit many attributes commonly associated with human experts performing difficult tasks and using specialized knowledge and sophisticated problem-solving strategies. Knowledge-based systems differ from conventional software such as database systems in that they are able to reason about data and draw conclusions employing heuristic rules. Heuristics embody human expertise in some knowledge domain and are sometimes characterized as the 'rules of thumb' that one acquires through practical experience and uses to solve everyday problems. Knowledge-based systems have been developed in a variety of fields, including medical disciplines. A decision support system has been assisting clinicians in areas such as infectious disease therapy for many years. For example, these systems can help radiologists formulate and evaluate diagnostic hypotheses by recalling associations between diseases and imaging findings. Although radiologic technology relies heavily on computers, it has been slow to develop a knowledge-based system to aid in diagnoses. These systems can be valuable interactive educational tools for medical students. In 1992, we developed a DOS-based Bonex, a menu-driven expert system for the differential diagnosis of bone tumours using PDC Prolog. It was a rule-based expert system that led the user through a menu of questions and generated a hard copy report and a list of diagnoses with an estimate of the likelihood of each. Bonex was presented at the 1992 Annual Meeting of the Radiological Society of North America (RSNA) in Chicago. We also developed an expert system for the differential diagnosis of brain lesions

Radiologic diagnosis of bone tumours using Webonex, a web-based artificial intelligence program

Energy Technology Data Exchange (ETDEWEB)

Rasuli, P. [Univ. of Ottawa, Dept. of Radiology, Ottawa Hospital, Ottawa, Ontario (Canada); Rasouli, F. [Research, Development and Engineering Center, PMUSA, Richmond, VA (United States); Rasouli, T. [Johns Hopkins Univ., Dept. of Cognitive Science, Baltimore, Maryland (United States)

2001-08-01

Knowledge-based system is a decision support system in which an expert's knowledge and reasoning can be applied to problems in bounded knowledge domains. These systems, using knowledge and inference techniques, mimic human reasoning to solve problems. Knowledge-based systems are said to be 'intelligent' because they possess massive stores of information and exhibit many attributes commonly associated with human experts performing difficult tasks and using specialized knowledge and sophisticated problem-solving strategies. Knowledge-based systems differ from conventional software such as database systems in that they are able to reason about data and draw conclusions employing heuristic rules. Heuristics embody human expertise in some knowledge domain and are sometimes characterized as the 'rules of thumb' that one acquires through practical experience and uses to solve everyday problems. Knowledge-based systems have been developed in a variety of fields, including medical disciplines. A decision support system has been assisting clinicians in areas such as infectious disease therapy for many years. For example, these systems can help radiologists formulate and evaluate diagnostic hypotheses by recalling associations between diseases and imaging findings. Although radiologic technology relies heavily on computers, it has been slow to develop a knowledge-based system to aid in diagnoses. These systems can be valuable interactive educational tools for medical students. In 1992, we developed a DOS-based Bonex, a menu-driven expert system for the differential diagnosis of bone tumours using PDC Prolog. It was a rule-based expert system that led the user through a menu of questions and generated a hard copy report and a list of diagnoses with an estimate of the likelihood of each. Bonex was presented at the 1992 Annual Meeting of the Radiological Society of North America (RSNA) in Chicago. We also developed an expert system for the differential

Reliable fault detection and diagnosis of photovoltaic systems based on statistical monitoring approaches

KAUST Repository

Harrou, Fouzi

2017-09-18

This study reports the development of an innovative fault detection and diagnosis scheme to monitor the direct current (DC) side of photovoltaic (PV) systems. Towards this end, we propose a statistical approach that exploits the advantages of one-diode model and those of the univariate and multivariate exponentially weighted moving average (EWMA) charts to better detect faults. Specifically, we generate array\\'s residuals of current, voltage and power using measured temperature and irradiance. These residuals capture the difference between the measurements and the predictions MPP for the current, voltage and power from the one-diode model, and use them as fault indicators. Then, we apply the multivariate EWMA (MEWMA) monitoring chart to the residuals to detect faults. However, a MEWMA scheme cannot identify the type of fault. Once a fault is detected in MEWMA chart, the univariate EWMA chart based on current and voltage indicators is used to identify the type of fault (e.g., short-circuit, open-circuit and shading faults). We applied this strategy to real data from the grid-connected PV system installed at the Renewable Energy Development Center, Algeria. Results show the capacity of the proposed strategy to monitors the DC side of PV systems and detects partial shading.

A novel computer-aided diagnosis system for breast MRI based on feature selection and ensemble learning.

Science.gov (United States)

Lu, Wei; Li, Zhe; Chu, Jinghui

2017-04-01

Breast cancer is a common cancer among women. With the development of modern medical science and information technology, medical imaging techniques have an increasingly important role in the early detection and diagnosis of breast cancer. In this paper, we propose an automated computer-aided diagnosis (CADx) framework for magnetic resonance imaging (MRI). The scheme consists of an ensemble of several machine learning-based techniques, including ensemble under-sampling (EUS) for imbalanced data processing, the Relief algorithm for feature selection, the subspace method for providing data diversity, and Adaboost for improving the performance of base classifiers. We extracted morphological, various texture, and Gabor features. To clarify the feature subsets' physical meaning, subspaces are built by combining morphological features with each kind of texture or Gabor feature. We tested our proposal using a manually segmented Region of Interest (ROI) data set, which contains 438 images of malignant tumors and 1898 images of normal tissues or benign tumors. Our proposal achieves an area under the ROC curve (AUC) value of 0.9617, which outperforms most other state-of-the-art breast MRI CADx systems. Compared with other methods, our proposal significantly reduces the false-positive classification rate. Copyright © 2017 Elsevier Ltd. All rights reserved.

Computer-Aided Diagnosis Based on Convolutional Neural Network System for Colorectal Polyp Classification: Preliminary Experience.

Science.gov (United States)

Komeda, Yoriaki; Handa, Hisashi; Watanabe, Tomohiro; Nomura, Takanobu; Kitahashi, Misaki; Sakurai, Toshiharu; Okamoto, Ayana; Minami, Tomohiro; Kono, Masashi; Arizumi, Tadaaki; Takenaka, Mamoru; Hagiwara, Satoru; Matsui, Shigenaga; Nishida, Naoshi; Kashida, Hiroshi; Kudo, Masatoshi

2017-01-01

Computer-aided diagnosis (CAD) is becoming a next-generation tool for the diagnosis of human disease. CAD for colon polyps has been suggested as a particularly useful tool for trainee colonoscopists, as the use of a CAD system avoids the complications associated with endoscopic resections. In addition to conventional CAD, a convolutional neural network (CNN) system utilizing artificial intelligence (AI) has been developing rapidly over the past 5 years. We attempted to generate a unique CNN-CAD system with an AI function that studied endoscopic images extracted from movies obtained with colonoscopes used in routine examinations. Here, we report our preliminary results of this novel CNN-CAD system for the diagnosis of colon polyps. A total of 1,200 images from cases of colonoscopy performed between January 2010 and December 2016 at Kindai University Hospital were used. These images were extracted from the video of actual endoscopic examinations. Additional video images from 10 cases of unlearned processes were retrospectively assessed in a pilot study. They were simply diagnosed as either an adenomatous or nonadenomatous polyp. The number of images used by AI to learn to distinguish adenomatous from nonadenomatous was 1,200:600. These images were extracted from the videos of actual endoscopic examinations. The size of each image was adjusted to 256 × 256 pixels. A 10-hold cross-validation was carried out. The accuracy of the 10-hold cross-validation is 0.751, where the accuracy is the ratio of the number of correct answers over the number of all the answers produced by the CNN. The decisions by the CNN were correct in 7 of 10 cases. A CNN-CAD system using routine colonoscopy might be useful for the rapid diagnosis of colorectal polyp classification. Further prospective studies in an in vivo setting are required to confirm the effectiveness of a CNN-CAD system in routine colonoscopy. © 2017 S. Karger AG, Basel.

Tuberculosis-Diagnostic Expert System: an architecture for translating patients information from the web for use in tuberculosis diagnosis.

Science.gov (United States)

Osamor, Victor C; Azeta, Ambrose A; Ajulo, Oluseyi O

2014-12-01

Over 1.5-2 million tuberculosis deaths occur annually. Medical professionals are faced with a lot of challenges in delivering good health-care with unassisted automation in hospitals where there are several patients who need the doctor's attention. To automate the pre-laboratory screening process against tuberculosis infection to aid diagnosis and make it fast and accessible to the public via the Internet. The expert system we have built is designed to also take care of people who do not have access to medical experts, but would want to check their medical status. A rule-based approach has been used, and unified modeling language and the client-server architecture technique were applied to model the system and to develop it as a web-based expert system for tuberculosis diagnosis. Algorithmic rules in the Tuberculosis-Diagnosis Expert System necessitate decision coverage where tuberculosis is either suspected or not suspected. The architecture consists of a rule base, knowledge base, and patient database. These units interact with the inference engine, which receives patient' data through the Internet via a user interface. We present the architecture of the Tuberculosis-Diagnosis Expert System and its implementation. We evaluated it for usability to determine the level of effectiveness, efficiency and user satisfaction. The result of the usability evaluation reveals that the system has a usability of 4.08 out of a scale of 5. This is an indication of a more-than-average system performance. Several existing expert systems have been developed for the purpose of supporting different medical diagnoses, but none is designed to translate tuberculosis patients' symptomatic data for online pre-laboratory screening. Our Tuberculosis-Diagnosis Expert System is an effective solution for the implementation of the needed web-based expert system diagnosis. © The Author(s) 2013.

An Integrated Model-Based Distributed Diagnosis and Prognosis Framework

Data.gov (United States)

National Aeronautics and Space Administration — Diagnosis and prognosis are necessary tasks for system reconfiguration and fault-adaptive control in complex systems. Diagnosis consists of detec- tion, isolation...

Adaptive neural network/expert system that learns fault diagnosis for different structures

Science.gov (United States)

Simon, Solomon H.

1992-08-01

Corporations need better real-time monitoring and control systems to improve productivity by watching quality and increasing production flexibility. The innovative technology to achieve this goal is evolving in the form artificial intelligence and neural networks applied to sensor processing, fusion, and interpretation. By using these advanced Al techniques, we can leverage existing systems and add value to conventional techniques. Neural networks and knowledge-based expert systems can be combined into intelligent sensor systems which provide real-time monitoring, control, evaluation, and fault diagnosis for production systems. Neural network-based intelligent sensor systems are more reliable because they can provide continuous, non-destructive monitoring and inspection. Use of neural networks can result in sensor fusion and the ability to model highly, non-linear systems. Improved models can provide a foundation for more accurate performance parameters and predictions. We discuss a research software/hardware prototype which integrates neural networks, expert systems, and sensor technologies and which can adapt across a variety of structures to perform fault diagnosis. The flexibility and adaptability of the prototype in learning two structures is presented. Potential applications are discussed.

Active Fault Diagnosis in Sampled-data Systems

DEFF Research Database (Denmark)

Niemann, Hans Henrik; Poulsen, Niels Kjølstad

2015-01-01

The focus in this paper is on active fault diagnosis (AFD) in closed-loop sampleddata systems. Applying the same AFD architecture as for continuous-time systems does not directly result in the same set of closed-loop matrix transfer functions. For continuous-time systems, the LFT (linear fractional...... transformation) structure in the connection between the parametric faults and the matrix transfer function (also known as the fault signature matrix) applied for AFD is not directly preserved for sampled-data system. As a consequence of this, the AFD methods cannot directly be applied for sampled-data systems....... Two methods are considered in this paper to handle the fault signature matrix for sampled-data systems such that standard AFD methods can be applied. The first method is based on a discretization of the system such that the LFT structure is preserved resulting in the same LFT structure in the fault...

Clinical Assistant Diagnosis for Electronic Medical Record Based on Convolutional Neural Network.

Science.gov (United States)

Yang, Zhongliang; Huang, Yongfeng; Jiang, Yiran; Sun, Yuxi; Zhang, Yu-Jin; Luo, Pengcheng

2018-04-20

Automatically extracting useful information from electronic medical records along with conducting disease diagnoses is a promising task for both clinical decision support(CDS) and neural language processing(NLP). Most of the existing systems are based on artificially constructed knowledge bases, and then auxiliary diagnosis is done by rule matching. In this study, we present a clinical intelligent decision approach based on Convolutional Neural Networks(CNN), which can automatically extract high-level semantic information of electronic medical records and then perform automatic diagnosis without artificial construction of rules or knowledge bases. We use collected 18,590 copies of the real-world clinical electronic medical records to train and test the proposed model. Experimental results show that the proposed model can achieve 98.67% accuracy and 96.02% recall, which strongly supports that using convolutional neural network to automatically learn high-level semantic features of electronic medical records and then conduct assist diagnosis is feasible and effective.

Fault diagnosis

Science.gov (United States)

Abbott, Kathy

1990-01-01

The objective of the research in this area of fault management is to develop and implement a decision aiding concept for diagnosing faults, especially faults which are difficult for pilots to identify, and to develop methods for presenting the diagnosis information to the flight crew in a timely and comprehensible manner. The requirements for the diagnosis concept were identified by interviewing pilots, analyzing actual incident and accident cases, and examining psychology literature on how humans perform diagnosis. The diagnosis decision aiding concept developed based on those requirements takes abnormal sensor readings as input, as identified by a fault monitor. Based on these abnormal sensor readings, the diagnosis concept identifies the cause or source of the fault and all components affected by the fault. This concept was implemented for diagnosis of aircraft propulsion and hydraulic subsystems in a computer program called Draphys (Diagnostic Reasoning About Physical Systems). Draphys is unique in two important ways. First, it uses models of both functional and physical relationships in the subsystems. Using both models enables the diagnostic reasoning to identify the fault propagation as the faulted system continues to operate, and to diagnose physical damage. Draphys also reasons about behavior of the faulted system over time, to eliminate possibilities as more information becomes available, and to update the system status as more components are affected by the fault. The crew interface research is examining display issues associated with presenting diagnosis information to the flight crew. One study examined issues for presenting system status information. One lesson learned from that study was that pilots found fault situations to be more complex if they involved multiple subsystems. Another was pilots could identify the faulted systems more quickly if the system status was presented in pictorial or text format. Another study is currently under way to

A study on the development of an automatic fault diagnosis system for testing NPP digital electronic circuits

International Nuclear Information System (INIS)

Kim, Dae Sik

1993-02-01

This paper describes a study on the development of an automatic fault diagnosis system for testing digital electronic circuits of nuclear power plants. Compared with the other conventional fault diagnosis systems, the system described in this paper uses Artificial Intelligence technique of model based reasoning and corroboration, which makes fault diagnosis much more efficient. In order to reduce the testing time, an optimal testing set which means a minimal testing set to determine whether or not the circuit is fault-free and to locate the faulty gate was derived. Compared with the testing using an exhaustive testing set, the testing using the optimal testing set makes fault diagnosis much more fast. Since the system diagnoses the circuit boards bases only on input and output signals, it can be further developed for on-line testing. The system was implemented on a microprocessor and was applied for Universal Circuit board testing of the Solid State protection System in nuclear power plants

Virtual reality system for diagnosis and therapeutic planning of cerebral aneurysms.

Science.gov (United States)

Mo, Da-peng; Bao, Sheng-de; Li, Liang; Yi, Zhi-qiang; Zhang, Jia-yong; Zhang, Yang

2010-08-01

The virtual reality (VR) system can provide the neurosurgeon to intuitively interact with and manipulate the three dimensional (3-D) image similarly to manipulate a real object. It was seldom reported that the system was used in diagnosis and treatment of cerebral aneurysms. This study aimed to investigate the application of VR system in diagnosis and therapeutic planning of cerebral aneurysms. A total of 24 cases of cerebral aneurysms were enrolled in this study from 2006 to 2008, which diagnosed by 3-D digital subtraction angiography (3D-DSA) or VR-based computed tomography angiographies (CTA). The VR system and 3D-DSA system were used to observe and measure aneurysms and the adjacent vessels. The data of observation and measurements were compared between VR image and 3D-DSA image. All the patients underwent surgical plan and simulated neurosurgical procedures in the VR system. There were 28 aneurysms detected in VR system and 3D-DSA system. The VR system generated clear and vivid 3-D virtual images which clearly displayed the location and size of the aneurysms and their precise anatomical spatial relations to the parent arteries and skull. The location, size and shape of the aneurysms and their anatomical relationship with the adjacent vessels were similar between 3-D virtual image and 3D-DSA, but the spatial relationship between aneurysms and skull only been displayed by VR system. This VR system also could simulate simple surgical procedures and surgical environments. The VR system can provide a highly effective way to provide precise imaging details as same as 3D-DSA system and assist the diagnosis of cerebral aneurysms with virtual 3-D data based on CTA. It significantly enhances the chosen therapeutic strategy of cerebral aneurysms.

Fault diagnosis of air conditioning systems based on qualitative bond graph

International Nuclear Information System (INIS)

Ghiaus, C.

1999-01-01

The bond graph method represents a unified approach for modeling engineering systems. The main idea is that power transfer bonds the components of a system. The bond graph model is the same for both quantitative representation, in which parameters have numerical values, and qualitative approach, in which they are classified qualitatively. To infer the cause of faults using a qualitative method, a system of qualitative equations must be solved. However, the characteristics of qualitative operators require specific methods for solving systems of equations having qualitative variables. This paper proposes both a method for recursively solving the qualitative system of equations derived from bond graph, and a bond graph model of a direct-expansion, mechanical vapor-compression air conditioning system. Results from diagnosing two faults in a real air conditioning system are presented and discussed. Occasionally, more than one fault candidate is inferred for the same set of qualitative values derived from measurements. In these cases, additional information is required to localize the fault. Fault diagnosis is initiated by a fault detection mechanism which also classifies the quantitative measurements into qualitative values; the fault detection is not presented here. (author)

Further substantial improvement of interventional diagnosis and treatment via portal vein system

International Nuclear Information System (INIS)

Yang Weizhu; Chen Yongde

2006-01-01

Along with the development of interventional appliances and proficiency of operational skills, the interventional diagnosis and treatment via hepatic portal vein system have achieved great progress and improvement. However, in order to further exploit the advantages of interventional diagnosis and treatment, the review of the anatomical structures, normal aberrance of portal venous system were needed. Getting familiar with pathologic condition to discover the new interventional appliances and embolic agents, and then in term of conduct the research on a very tough substantial base in a down-to-earth manner were important. (authors)

Study on fault diagnosis and load feedback control system of combine harvester

Science.gov (United States)

Li, Ying; Wang, Kun

2017-01-01

In order to timely gain working status parameters of operating parts in combine harvester and improve its operating efficiency, fault diagnosis and load feedback control system is designed. In the system, rotation speed sensors were used to gather these signals of forward speed and rotation speeds of intermediate shaft, conveying trough, tangential and longitudinal flow threshing rotors, grain conveying auger. Using C8051 single chip microcomputer (SCM) as processor for main control unit, faults diagnosis and forward speed control were carried through by rotation speed ratio analysis of each channel rotation speed and intermediate shaft rotation speed by use of multi-sensor fused fuzzy control algorithm, and these processing results would be sent to touch screen and display work status of combine harvester. Field trials manifest that fault monitoring and load feedback control system has good man-machine interaction and the fault diagnosis method based on rotation speed ratios has low false alarm rate, and the system can realize automation control of forward speed for combine harvester.

A Clinical Approach to the Diagnosis of Acid-Base Disorders

OpenAIRE

Bear, Robert A.

1986-01-01

The ability to diagnose and manage acid-base disorders rapidly and effectively is essential to the care of critically ill patients. This article presents an approach to the diagnosis of pure and mixed acid-base disorders, metabolic or respiratory. The approach taken is based on using the law of mass-action equation as it applies to the bicarbonate buffer system (Henderson equation), using sub-classifications for diagnostic purposes of causes of metabolic acidosis and metabolic alkalosis, and ...

Plant experience with an expert system for alarm diagnosis

International Nuclear Information System (INIS)

Gimmy, K.L.

1986-01-01

An expert system called Diagnosis of Multiple Alarms (DMA) is in routine use at four nuclear reactors operated by the DuPont Company. The system is wired to plant alarm annunciators and does event-tree analysis to see if a pattern exists. Any diagnosis is displayed to the plant operator and the corrective procedure to be followed is also identified. The display is automatically superseded if a higher priority diagnosis is made. The system is integrated with operator training and procedures. Operating results have been positive. DMA has diagnosed several hard-to-locate small leaks. There have been some false diagnosis, and realistic plant environments must be considered in such expert systems. 2 refs., 5 figs

Fault Diagnosis of Nonlinear Systems Using Structured Augmented State Models

Institute of Scientific and Technical Information of China (English)

Jochen Aβfalg; Frank Allg(o)wer

2007-01-01

This paper presents an internal model approach for modeling and diagnostic functionality design for nonlinear systems operating subject to single- and multiple-faults. We therefore provide the framework of structured augmented state models. Fault characteristics are considered to be generated by dynamical exosystems that are switched via equality constraints to overcome the augmented state observability limiting the number of diagnosable faults. Based on the proposed model, the fault diagnosis problem is specified as an optimal hybrid augmented state estimation problem. Sub-optimal solutions are motivated and exemplified for the fault diagnosis of the well-known three-tank benchmark. As the considered class of fault diagnosis problems is large, the suggested approach is not only of theoretical interest but also of high practical relevance.

Early results of pediatric appendicitis after adoption of diagnosis-related group-based payment system in South Korea

Directory of Open Access Journals (Sweden)

Moon SB

2015-11-01

Full Text Available Suk-Bae MoonDepartment of Surgery, Kangwon National University Hospital, Kangwon National School of Medicine, Kangwon National University, Chuncheon, South KoreaPurpose: As an alternative to the existing fee-for-service (FFS system, a diagnosis-related group (DRG-based payment system has been suggested. The aim of this study was to investigate the early results of pediatric appendicitis treatment under the DRG system, focusing on health care expenditure and quality of health care services.Patients and methods: The medical records of 60 patients, 30 patients before (FFS group, and 30 patients after adoption of the DRG system (DRG, were reviewed retrospectively.Results: Mean hospital stay was shortened, but the complication and readmission rates did not worsen in the DRG. Overall health care expenditure and self-payment decreased from Korean Won (KRW 2,499,935 and KRW 985,540, respectively, in the FFS group to KRW 2,386,552 and KRW 492,920, respectively, in the DRG. The insurer’s payment increased from KRW 1,514,395 in the FFS group to KRW 1,893,632 in the DRG. For patients in the DRG, calculation by the DRG system yielded greater overall expenditure (KRW 2,020,209 vs KRW 2,386,552 but lower self-payment (KRW 577,803 vs KRW 492,920 than calculation by the FFS system.Conclusion: The DRG system worked well in pediatric patients with acute appendicitis in terms of cost-effectiveness over the short term. The gradual burden on the national health insurance fund should be taken into consideration.Keywords: appendicitis, child, fee-for-service plans, diagnosis-related groups, quality of health care, health care expenditures

Diagnosis-related group (DRG)-based case-mix funding system, a promising alternative for fee for service payment in China.

Science.gov (United States)

Zhao, Cuirong; Wang, Chao; Shen, Chengwu; Wang, Qian

2018-05-13

Fee for services (FFS) is the prevailing method of payment in most Chinese public hospitals. Under this retrospective payment system, medical care providers are paid based on medical services and tend to over-treat to maximize their income, thereby contributing to rising medical costs and uncontrollable health expenditures to a large extent. Payment reform needs to be promptly implemented to move to a prospective payment plan. The diagnosis-related group (DRG)-based case-mix payment system, with its superior efficiency and containment of costs, has garnered increased attention and it represents a promising alternative. This article briefly describes the DRG-based case-mix payment system, it comparatively analyzes differences between FFS and case-mix funding systems, and it describes the implementation of DRGs in China. China's social and economic conditions differ across regions, so establishment of a national payment standard will take time and involve difficulties. No single method of provider payment is perfect. Measures to monitor and minimize the negative ethical implications and unintended effects of a DRG-based case-mix payment system are essential to ensuring the lasting social benefits of payment reform in Chinese public hospitals.

Porous TiO2-Based Gas Sensors for Cyber Chemical Systems to Provide Security and Medical Diagnosis

Science.gov (United States)

2017-01-01

Gas sensors play an important role in our life, providing control and security of technical processes, environment, transportation and healthcare. Consequently, the development of high performance gas sensor devices is the subject of intense research. TiO2, with its excellent physical and chemical properties, is a very attractive material for the fabrication of chemical sensors. Meanwhile, the emerging technologies are focused on the fabrication of more flexible and smart systems for precise monitoring and diagnosis in real-time. The proposed cyber chemical systems in this paper are based on the integration of cyber elements with the chemical sensor devices. These systems may have a crucial effect on the environmental and industrial safety, control of carriage of dangerous goods and medicine. This review highlights the recent developments on fabrication of porous TiO2-based chemical gas sensors for their application in cyber chemical system showing the convenience and feasibility of such a model to provide the security and to perform the diagnostics. The most of reports have demonstrated that the fabrication of doped, mixed and composite structures based on porous TiO2 may drastically improve its sensing performance. In addition, each component has its unique effect on the sensing properties of material. PMID:29257076

Case-Based Fault Diagnostic System

International Nuclear Information System (INIS)

Mohamed, A.H.

2014-01-01

Nowadays, case-based fault diagnostic (CBFD) systems have become important and widely applied problem solving technologies. They are based on the assumption that “similar faults have similar diagnosis”. On the other hand, CBFD systems still suffer from some limitations. Common ones of them are: (1) failure of CBFD to have the needed diagnosis for the new faults that have no similar cases in the case library. (2) Limited memorization when increasing the number of stored cases in the library. The proposed research introduces incorporating the neural network into the case based system to enable the system to diagnose all the faults. Neural networks have proved their success in the classification and diagnosis problems. The suggested system uses the neural network to diagnose the new faults (cases) that cannot be diagnosed by the traditional CBR diagnostic system. Besides, the proposed system can use the another neural network to control adding and deleting the cases in the library to manage the size of the cases in the case library. However, the suggested system has improved the performance of the case based fault diagnostic system when applied for the motor rolling bearing as a case of study

Fault Diagnosis for Rolling Bearings under Variable Conditions Based on Visual Cognition.

Science.gov (United States)

Cheng, Yujie; Zhou, Bo; Lu, Chen; Yang, Chao

2017-05-25

Fault diagnosis for rolling bearings has attracted increasing attention in recent years. However, few studies have focused on fault diagnosis for rolling bearings under variable conditions. This paper introduces a fault diagnosis method for rolling bearings under variable conditions based on visual cognition. The proposed method includes the following steps. First, the vibration signal data are transformed into a recurrence plot (RP), which is a two-dimensional image. Then, inspired by the visual invariance characteristic of the human visual system (HVS), we utilize speed up robust feature to extract fault features from the two-dimensional RP and generate a 64-dimensional feature vector, which is invariant to image translation, rotation, scaling variation, etc. Third, based on the manifold perception characteristic of HVS, isometric mapping, a manifold learning method that can reflect the intrinsic manifold embedded in the high-dimensional space, is employed to obtain a low-dimensional feature vector. Finally, a classical classification method, support vector machine, is utilized to realize fault diagnosis. Verification data were collected from Case Western Reserve University Bearing Data Center, and the experimental result indicates that the proposed fault diagnosis method based on visual cognition is highly effective for rolling bearings under variable conditions, thus providing a promising approach from the cognitive computing field.

Flu Diagnosis System Using Jaccard Index and Rough Set Approaches

Science.gov (United States)

Efendi, Riswan; Azah Samsudin, Noor; Mat Deris, Mustafa; Guan Ting, Yip

2018-04-01

Jaccard index and rough set approaches have been frequently implemented in decision support systems with various domain applications. Both approaches are appropriate to be considered for categorical data analysis. This paper presents the applications of sets operations for flu diagnosis systems based on two different approaches, such as, Jaccard index and rough set. These two different approaches are established using set operations concept, namely intersection and subset. The step-by-step procedure is demonstrated from each approach in diagnosing flu system. The similarity and dissimilarity indexes between conditional symptoms and decision are measured using Jaccard approach. Additionally, the rough set is used to build decision support rules. Moreover, the decision support rules are established using redundant data analysis and elimination of unclassified elements. A number data sets is considered to attempt the step-by-step procedure from each approach. The result has shown that rough set can be used to support Jaccard approaches in establishing decision support rules. Additionally, Jaccard index is better approach for investigating the worst condition of patients. While, the definitely and possibly patients with or without flu can be determined using rough set approach. The rules may improve the performance of medical diagnosis systems. Therefore, inexperienced doctors and patients are easier in preliminary flu diagnosis.

Intelligent Data Visualization for Cross-Checking Spacecraft System Diagnosis

Science.gov (United States)

Ong, James C.; Remolina, Emilio; Breeden, David; Stroozas, Brett A.; Mohammed, John L.

2012-01-01

Any reasoning system is fallible, so crew members and flight controllers must be able to cross-check automated diagnoses of spacecraft or habitat problems by considering alternate diagnoses and analyzing related evidence. Cross-checking improves diagnostic accuracy because people can apply information processing heuristics, pattern recognition techniques, and reasoning methods that the automated diagnostic system may not possess. Over time, cross-checking also enables crew members to become comfortable with how the diagnostic reasoning system performs, so the system can earn the crew s trust. We developed intelligent data visualization software that helps users cross-check automated diagnoses of system faults more effectively. The user interface displays scrollable arrays of timelines and time-series graphs, which are tightly integrated with an interactive, color-coded system schematic to show important spatial-temporal data patterns. Signal processing and rule-based diagnostic reasoning automatically identify alternate hypotheses and data patterns that support or rebut the original and alternate diagnoses. A color-coded matrix display summarizes the supporting or rebutting evidence for each diagnosis, and a drill-down capability enables crew members to quickly view graphs and timelines of the underlying data. This system demonstrates that modest amounts of diagnostic reasoning, combined with interactive, information-dense data visualizations, can accelerate system diagnosis and cross-checking.

The Malaria System MicroApp: A New, Mobile Device-Based Tool for Malaria Diagnosis.

Science.gov (United States)

Oliveira, Allisson Dantas; Prats, Clara; Espasa, Mateu; Zarzuela Serrat, Francesc; Montañola Sales, Cristina; Silgado, Aroa; Codina, Daniel Lopez; Arruda, Mercia Eliane; I Prat, Jordi Gomez; Albuquerque, Jones

2017-04-25

Malaria is a public health problem that affects remote areas worldwide. Climate change has contributed to the problem by allowing for the survival of Anopheles in previously uninhabited areas. As such, several groups have made developing news systems for the automated diagnosis of malaria a priority. The objective of this study was to develop a new, automated, mobile device-based diagnostic system for malaria. The system uses Giemsa-stained peripheral blood samples combined with light microscopy to identify the Plasmodium falciparum species in the ring stage of development. The system uses image processing and artificial intelligence techniques as well as a known face detection algorithm to identify Plasmodium parasites. The algorithm is based on integral image and haar-like features concepts, and makes use of weak classifiers with adaptive boosting learning. The search scope of the learning algorithm is reduced in the preprocessing step by removing the background around blood cells. As a proof of concept experiment, the tool was used on 555 malaria-positive and 777 malaria-negative previously-made slides. The accuracy of the system was, on average, 91%, meaning that for every 100 parasite-infected samples, 91 were identified correctly. Accessibility barriers of low-resource countries can be addressed with low-cost diagnostic tools. Our system, developed for mobile devices (mobile phones and tablets), addresses this by enabling access to health centers in remote communities, and importantly, not depending on extensive malaria expertise or expensive diagnostic detection equipment. ©Allisson Dantas Oliveira, Clara Prats, Mateu Espasa, Francesc Zarzuela Serrat, Cristina Montañola Sales, Aroa Silgado, Daniel Lopez Codina, Mercia Eliane Arruda, Jordi Gomez i Prat, Jones Albuquerque. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 25.04.2017.

Multiscale Permutation Entropy Based Rolling Bearing Fault Diagnosis

Directory of Open Access Journals (Sweden)

Jinde Zheng

2014-01-01

Full Text Available A new rolling bearing fault diagnosis approach based on multiscale permutation entropy (MPE, Laplacian score (LS, and support vector machines (SVMs is proposed in this paper. Permutation entropy (PE was recently proposed and defined to measure the randomicity and detect dynamical changes of time series. However, for the complexity of mechanical systems, the randomicity and dynamic changes of the vibration signal will exist in different scales. Thus, the definition of MPE is introduced and employed to extract the nonlinear fault characteristics from the bearing vibration signal in different scales. Besides, the SVM is utilized to accomplish the fault feature classification to fulfill diagnostic procedure automatically. Meanwhile, in order to avoid a high dimension of features, the Laplacian score (LS is used to refine the feature vector by ranking the features according to their importance and correlations with the main fault information. Finally, the rolling bearing fault diagnosis method based on MPE, LS, and SVM is proposed and applied to the experimental data. The experimental data analysis results indicate that the proposed method could identify the fault categories effectively.

An Approach to Management of Health Care and Medical Diagnosis Using of a Hybrid Disease Diagnosis System

Directory of Open Access Journals (Sweden)

Hodjat Hamidi

2017-02-01

Full Text Available Introduction: In order to simplify the information exchange within the medical diagnosis process, a collaborative software agent’s framework is presented. The purpose of the framework is to allow the automated information exchange between different medicine specialists. Methods: This study presented architecture of a hybrid disease diagnosis system. The architecture employed a learning algorithm and used soft computing to build a medical knowledge base. These machine intelligences are combined in a complementary approach to overcome the weakness of each other. To evaluate the hybrid learning algorithm and compare it with other methods, 699 samples were used in each experiment, where 60% was for training, 20% was for cross validation, and 20% for testing. Results: The results were obtained from the experiments on the breast cancer dataset. Different methods of soft computing system were merged to create diagnostic software functionality. As it is shown in the structure, the system has the ability to learn and collect knowledge that can be used in the detection of new images. Currently, the system is at the design stage. The system is to evaluate the performance of hybrid learning algorithm. The preliminary results showed a better performance of this system than other methods. However, the results can be tested with hybrid system on larger data sets to improve hybrid learning algorithm. Conclusion: The purpose of this paper was to simplify the diagnosis process of a patient by splitting the medical domain concepts (e.g., causes, effects, symptoms, tests in human body systems (e.g., respiratory, cardiovascular, though maintaining the holistic perspective through the links between common concepts.

A diagnosis method for physical systems using a multi-modeling approach

International Nuclear Information System (INIS)

Thetiot, R.

2000-01-01

In this thesis we propose a method for diagnosis problem solving. This method is based on a multi-modeling approach describing both normal and abnormal behavior of a system. This modeling approach allows to represent a system at different abstraction levels (behavioral, functional and teleological. Fundamental knowledge is described according to a bond-graph representation. We show that bond-graph representation can be exploited in order to generate (completely or partially) the functional models. The different models of the multi-modeling approach allows to define the functional state of a system at different abstraction levels. We exploit this property to exonerate sub-systems for which the expected behavior is observed. The behavioral and functional descriptions of the remaining sub-systems are exploited hierarchically in a two steps process. In a first step, the abnormal behaviors explaining some observations are identified. In a second step, the remaining unexplained observations are used to generate conflict sets and thus the consistency based diagnoses. The modeling method and the diagnosis process have been applied to a Reactor Coolant Pump Sets. This application illustrates the concepts described in this thesis and shows its potentialities. (authors)

Water chemistry diagnosis system for nuclear power plants

International Nuclear Information System (INIS)

Igarashi, Hiroo; Koya, Hiroshi; Osumi, Katsumi.

1990-01-01

The water quality control for the BWRs in Japan has advanced rapidly recently, and as to the dose reduction due to the decrease of radioactivity, Japan takes the position leading the world. In the background of the advanced water quality control like this and the increase of nuclear power plants in operation, the automation of arranging a large quantity of water quality control information and the heightening of its reliability have been demanded. Hitachi group developed the water quality synthetic control system which comprises the water quality data management system to process a large quantity of water quality data with a computer and the water quality diagnosis system to evaluate the state of operation of the plants by the minute change of water quality and to carry out the operational guide in the aspect of water quality control. To this water quality diagnosis system, high speed fuzzy inference is applied in order to do rapid diagnosis with fuzzy data. The trend of development of water quality control system, the construction of the water quality synthetic control system, the configuration of the water quality diagnosis system and the development of algorithm and the improvement of the reliability of maintenance are reported. (K.I.)

An intelligent system based on fuzzy probabilities for medical diagnosis – a study in aphasia diagnosis

Directory of Open Access Journals (Sweden)

Majid Moshtagh Khorasani

2009-04-01

Full Text Available

• BACKGROUND: Aphasia diagnosis is particularly challenging due to the linguistic uncertainty and vagueness, inconsistencies in the definition of aphasic syndromes, large number of measurements with  mprecision, natural diversity and subjectivity in test objects as well as in opinions of experts who diagnose the disease.
• METHODS: Fuzzy probability is proposed here as the basic framework for handling the uncertainties in medical diagnosis and particularly aphasia diagnosis. To efficiently construct this fuzzy probabilistic mapping, statistical analysis is performed that constructs input membership functions as well as determines an effective set of input features.
• RESULTS: Considering the high sensitivity of performance measures to different distribution of testing/training sets, a statistical t-test of significance is applied to compare fuzzy approach results with NN  esults as well as author’s earlier work using fuzzy logic. The proposed fuzzy probability estimator approach clearly provides better diagnosis for both classes of data sets. Specifically, for the first and second type of fuzzy probability classifiers, i.e. spontaneous speech and comprehensive model, P-values are 2.24E-08 and 0.0059, espectively, strongly rejecting the null hypothesis.
• CONCLUSIONS: The technique is applied and compared on both comprehensive and spontaneous speech test data for diagnosis of four Aphasia types: Anomic, Broca, Global and Wernicke. Statistical analysis confirms that the proposed approach can significantly improve accuracy using fewer Aphasia features.
• KEYWORDS: Aphasia, fuzzy probability, fuzzy logic, medical diagnosis, fuzzy rules.

A Fault Diagnosis Model of Surface to Air Missile Equipment Based on Wavelet Transformation and Support Vector Machine

Directory of Open Access Journals (Sweden)

Zhheng Ni

2016-01-01

Full Text Available At present, the fault signals of surface to air missile equipment are hard to collect and the accuracy of fault diagnosis is very low. To solve the above problems, based on the superiority of wavelet transformation on processing non-stationary signals and the advantage of SVM on pattern classification, this paper proposes a fault diagnosis model and takes the typical analog circuit diagnosis of one power distribution system as an example to verify the fault diagnosis model based on Wavelet Transformation and SVM. The simulation results show that the model is able to achieve fault diagnosis based on a small amount of training samples, which improves the accuracy of fault diagnosis.

Condition based monitoring, diagnosis and maintenance on operating equipments of a hydraulic generator unit

International Nuclear Information System (INIS)

Liu, X T; Feng, F Z; Si, A W

2012-01-01

According to performance characteristics of operating equipments in a hydraulic generator unit (HGU), the relative techniques on condition monitoring and fault diagnosis (CMFD) are introduced in this paper, especially the key technologies are emphasized, such as equipment monitoring, expert system (ES), intelligent diagnosis and condition based maintenance (CBM). Meanwhile, according to the instructor on CBM proposed by State electric power corporation, based on integrated mode, the main steps on implementation of CBM are discussed in this paper.

New scoring system for intra-abdominal injury diagnosis after blunt trauma.

Science.gov (United States)

Shojaee, Majid; Faridaalaee, Gholamreza; Yousefifard, Mahmoud; Yaseri, Mehdi; Arhami Dolatabadi, Ali; Sabzghabaei, Anita; Malekirastekenari, Ali

2014-01-01

An accurate scoring system for intra-abdominal injury (IAI) based on clinical manifestation and examination may decrease unnecessary CT scans, save time, and reduce healthcare cost. This study is designed to provide a new scoring system for a better diagnosis of IAI after blunt trauma. This prospective observational study was performed from April 2011 to October 2012 on patients aged above 18 years and suspected with blunt abdominal trauma (BAT) admitted to the emergency department (ED) of Imam Hussein Hospital and Shohadaye Hafte Tir Hospital. All patients were assessed and treated based on Advanced Trauma Life Support and ED protocol. Diagnosis was done according to CT scan findings, which was considered as the gold standard. Data were gathered based on patient's history, physical exam, ultrasound and CT scan findings by a general practitioner who was not blind to this study. Chi-square test and logistic regression were done. Factors with significant relationship with CT scan were imported in multivariate regression models, where a coefficient (β) was given based on the contribution of each of them. Scoring system was developed based on the obtained total β of each factor. Altogether 261 patients (80.1% male) were enrolled (48 cases of IAI). A 24-point blunt abdominal trauma scoring system (BATSS) was developed. Patients were divided into three groups including low (scoretool for BAT detection and has the potential to reduce unnecessary CT scan and cut unnecessary costs.

Model-Based Methods for Fault Diagnosis: Some Guide-Lines

DEFF Research Database (Denmark)

Patton, R.J.; Chen, J.; Nielsen, S.B.

1995-01-01

This paper provides a review of model-based fault diagnosis techniques. Starting from basic principles, the properties.......This paper provides a review of model-based fault diagnosis techniques. Starting from basic principles, the properties....

A hierarchical fuzzy rule-based approach to aphasia diagnosis.

Science.gov (United States)

Akbarzadeh-T, Mohammad-R; Moshtagh-Khorasani, Majid

2007-10-01

Aphasia diagnosis is a particularly challenging medical diagnostic task due to the linguistic uncertainty and vagueness, inconsistencies in the definition of aphasic syndromes, large number of measurements with imprecision, natural diversity and subjectivity in test objects as well as in opinions of experts who diagnose the disease. To efficiently address this diagnostic process, a hierarchical fuzzy rule-based structure is proposed here that considers the effect of different features of aphasia by statistical analysis in its construction. This approach can be efficient for diagnosis of aphasia and possibly other medical diagnostic applications due to its fuzzy and hierarchical reasoning construction. Initially, the symptoms of the disease which each consists of different features are analyzed statistically. The measured statistical parameters from the training set are then used to define membership functions and the fuzzy rules. The resulting two-layered fuzzy rule-based system is then compared with a back propagating feed-forward neural network for diagnosis of four Aphasia types: Anomic, Broca, Global and Wernicke. In order to reduce the number of required inputs, the technique is applied and compared on both comprehensive and spontaneous speech tests. Statistical t-test analysis confirms that the proposed approach uses fewer Aphasia features while also presenting a significant improvement in terms of accuracy.

Fault Diagnosis for Actuators in a Class of Nonlinear Systems Based on an Adaptive Fault Detection Observer

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Runxia Guo

2016-01-01

Full Text Available The problem of actuators’ fault diagnosis is pursued for a class of nonlinear control systems that are affected by bounded measurement noise and external disturbances. A novel fault diagnosis algorithm has been proposed by combining the idea of adaptive control theory and the approach of fault detection observer. The asymptotical stability of the fault detection observer is guaranteed by setting the adaptive adjusting law of the unknown fault vector. A theoretically rigorous proof of asymptotical stability has been given. Under the condition that random measurement noise generated by the sensors of control systems and external disturbances exist simultaneously, the designed fault diagnosis algorithm is able to successfully give specific estimated values of state variables and failures rather than just giving a simple fault warning. Moreover, the proposed algorithm is very simple and concise and is easy to be applied to practical engineering. Numerical experiments are carried out to evaluate the performance of the fault diagnosis algorithm. Experimental results show that the proposed diagnostic strategy has a satisfactory estimation effect.

The Design and Implementation of a Remote Fault Reasoning Diagnosis System for Meteorological Satellites Data Acquisition

Directory of Open Access Journals (Sweden)

Zhu Jie

2017-01-01

Full Text Available Under the background of the trouble shooting requirements of FENGYUN-3 (FY-3 meteorological satellites data acquisition in domestic and oversea ground stations, a remote fault reasoning diagnosis system is developed by Java 1.6 in eclipse 3.6 platform. The general framework is analyzed, the workflow is introduced. Based on the system, it can realize the remote and centralized monitoring of equipment running status in ground stations，triggering automatic fault diagnosis and rule based fault reasoning by parsing the equipment quality logs, generating trouble tickets and importing expert experience database, providing text and graphics query methods. Through the practical verification, the system can assist knowledge engineers in remote precise and rapid fault location with friendly graphical user interface, boost the fault diagnosis efficiency, enhance the remote monitoring ability of integrity operating control system. The system has a certain practical significance to improve reliability of FY-3 meteorological satellites data acquisition.

An Integrated Framework for Model-Based Distributed Diagnosis and Prognosis

Data.gov (United States)

National Aeronautics and Space Administration — Diagnosis and prognosis are necessary tasks for system re- configuration and fault-adaptive control in complex systems. Diagnosis consists of detection, isolation...

Computer Aided Diagnosis System for Early Lung Cancer Detection

Directory of Open Access Journals (Sweden)

Fatma Taher

2015-11-01

Full Text Available Lung cancer continues to rank as the leading cause of cancer deaths worldwide. One of the most promising techniques for early detection of cancerous cells relies on sputum cell analysis. This was the motivation behind the design and the development of a new computer aided diagnosis (CAD system for early detection of lung cancer based on the analysis of sputum color images. The proposed CAD system encompasses four main processing steps. First is the preprocessing step which utilizes a Bayesian classification method using histogram analysis. Then, in the second step, mean shift segmentation is applied to segment the nuclei from the cytoplasm. The third step is the feature analysis. In this step, geometric and chromatic features are extracted from the nucleus region. These features are used in the diagnostic process of the sputum images. Finally, the diagnosis is completed using an artificial neural network and support vector machine (SVM for classifying the cells into benign or malignant. The performance of the system was analyzed based on different criteria such as sensitivity, specificity and accuracy. The evaluation was carried out using Receiver Operating Characteristic (ROC curve. The experimental results demonstrate the efficiency of the SVM classifier over other classifiers, with 97% sensitivity and accuracy as well as a significant reduction in the number of false positive and false negative rates.

An Expert System-Based Approach to Hospitality Company Diagnosis

OpenAIRE

Balfe, Andrew; O'Connor, Peter; McDonnell, Ciaran

1994-01-01

This paper describes the development of a prototype Expert System-based Analysis and Diagnostic (ESAD) package for the Hotel and Catering Industry. This computerised tool aids the hospitality manager in methodically scrutinising the hotel unit and environment, combining key information with systematic reasoning. The system searches through its extensive knowledge base, investigating complicated relationships. The number of possibilities considered is increased which will broaden the depth and...

Image standards in Tissue-Based Diagnosis (Diagnostic Surgical Pathology

Directory of Open Access Journals (Sweden)

Vollmer Ekkehard

2008-04-01

Full Text Available Abstract Background Progress in automated image analysis, virtual microscopy, hospital information systems, and interdisciplinary data exchange require image standards to be applied in tissue-based diagnosis. Aims To describe the theoretical background, practical experiences and comparable solutions in other medical fields to promote image standards applicable for diagnostic pathology. Theory and experiences Images used in tissue-based diagnosis present with pathology – specific characteristics. It seems appropriate to discuss their characteristics and potential standardization in relation to the levels of hierarchy in which they appear. All levels can be divided into legal, medical, and technological properties. Standards applied to the first level include regulations or aims to be fulfilled. In legal properties, they have to regulate features of privacy, image documentation, transmission, and presentation; in medical properties, features of disease – image combination, human – diagnostics, automated information extraction, archive retrieval and access; and in technological properties features of image acquisition, display, formats, transfer speed, safety, and system dynamics. The next lower second level has to implement the prescriptions of the upper one, i.e. describe how they are implemented. Legal aspects should demand secure encryption for privacy of all patient related data, image archives that include all images used for diagnostics for a period of 10 years at minimum, accurate annotations of dates and viewing, and precise hardware and software information. Medical aspects should demand standardized patients' files such as DICOM 3 or HL 7 including history and previous examinations, information of image display hardware and software, of image resolution and fields of view, of relation between sizes of biological objects and image sizes, and of access to archives and retrieval. Technological aspects should deal with image

Nuclear power plant monitoring and fault diagnosis methods based on the artificial intelligence technique

International Nuclear Information System (INIS)

Yoshikawa, S.; Saiki, A.; Ugolini, D.; Ozawa, K.

1996-01-01

The main objective of this paper is to develop an advanced diagnosis system based on the artificial intelligence technique to monitor the operation and to improve the operational safety of nuclear power plants. Three different methods have been elaborated in this study: an artificial neural network local diagnosis (NN ds ) scheme that acting at the component level discriminates between normal and abnormal transients, a model-based diagnostic reasoning mechanism that combines a physical causal network model-based knowledge compiler (KC) that generates applicable diagnostic rules from widely accepted physical knowledge compiler (KC) that generates applicable diagnostic rules from widely accepted physical knowledge. Although the three methods have been developed and verified independently, they are highly correlated and, when connected together, form a effective and robust diagnosis and monitoring tool. (authors)

Fault diagnosis and performance evaluation for high current LIA based on radial basis function neural network

International Nuclear Information System (INIS)

Yang Xinglin; Wang Huacen; Chen Nan; Dai Wenhua; Li Jin

2006-01-01

High current linear induction accelerator (LIA) is a complicated experimental physics device. It is difficult to evaluate and predict its performance. this paper presents a method which combines wavelet packet transform and radial basis function (RBF) neural network to build fault diagnosis and performance evaluation in order to improve reliability of high current LIA. The signal characteristics vectors which are extracted based on energy parameters of wavelet packet transform can well present the temporal and steady features of pulsed power signal, and reduce data dimensions effectively. The fault diagnosis system for accelerating cell and the trend classification system for the beam current based on RBF networks can perform fault diagnosis and evaluation, and provide predictive information for precise maintenance of high current LIA. (authors)

Scattering transform and LSPTSVM based fault diagnosis of rotating machinery

Science.gov (United States)

Ma, Shangjun; Cheng, Bo; Shang, Zhaowei; Liu, Geng

2018-05-01

This paper proposes an algorithm for fault diagnosis of rotating machinery to overcome the shortcomings of classical techniques which are noise sensitive in feature extraction and time consuming for training. Based on the scattering transform and the least squares recursive projection twin support vector machine (LSPTSVM), the method has the advantages of high efficiency and insensitivity for noise signal. Using the energy of the scattering coefficients in each sub-band, the features of the vibration signals are obtained. Then, an LSPTSVM classifier is used for fault diagnosis. The new method is compared with other common methods including the proximal support vector machine, the standard support vector machine and multi-scale theory by using fault data for two systems, a motor bearing and a gear box. The results show that the new method proposed in this study is more effective for fault diagnosis of rotating machinery.

Research on alarm triggered fault-diagnosis expert system for U-shaped tube breaking accident of steam generators

International Nuclear Information System (INIS)

Qian Hong; Luo Jianbo; Jin Weixiao; Zhou Jinming; Wang Du

2015-01-01

According to the U-shaped tube breaking accident of steam generator (SGTR), this paper designs a fault-diagnosis expert system based on the alarm triggering. By analyzing the fault mechanism of SGTR accidents, the fault symptom is obtained. The parameters of the belief rule are set up based on the simulation experiment. The information fusion is conducted on the fault-diagnosis results from multiple expert systems to obtain the final diagnose result. The test result shows that the expert system can diagnose the SGTR accident accurately and rapidly, and provide with the operation guidance. (authors)

A network-based biomarker approach for molecular investigation and diagnosis of lung cancer

Directory of Open Access Journals (Sweden)

Chen Bor-Sen

2011-01-01

Full Text Available Abstract Background Lung cancer is the leading cause of cancer deaths worldwide. Many studies have investigated the carcinogenic process and identified the biomarkers for signature classification. However, based on the research dedicated to this field, there is no highly sensitive network-based method for carcinogenesis characterization and diagnosis from the systems perspective. Methods In this study, a systems biology approach integrating microarray gene expression profiles and protein-protein interaction information was proposed to develop a network-based biomarker for molecular investigation into the network mechanism of lung carcinogenesis and diagnosis of lung cancer. The network-based biomarker consists of two protein association networks constructed for cancer samples and non-cancer samples. Results Based on the network-based biomarker, a total of 40 significant proteins in lung carcinogenesis were identified with carcinogenesis relevance values (CRVs. In addition, the network-based biomarker, acting as the screening test, proved to be effective in diagnosing smokers with signs of lung cancer. Conclusions A network-based biomarker using constructed protein association networks is a useful tool to highlight the pathways and mechanisms of the lung carcinogenic process and, more importantly, provides potential therapeutic targets to combat cancer.

Research on Model-Based Fault Diagnosis for a Gas Turbine Based on Transient Performance

Directory of Open Access Journals (Sweden)

Detang Zeng

2018-01-01

Full Text Available It is essential to monitor and to diagnose faults in rotating machinery with a high thrust–weight ratio and complex structure for a variety of industrial applications, for which reliable signal measurements are required. However, the measured values consist of the true values of the parameters, the inertia of measurements, random errors and systematic errors. Such signals cannot reflect the true performance state and the health state of rotating machinery accurately. High-quality, steady-state measurements are necessary for most current diagnostic methods. Unfortunately, it is hard to obtain these kinds of measurements for most rotating machinery. Diagnosis based on transient performance is a useful tool that can potentially solve this problem. A model-based fault diagnosis method for gas turbines based on transient performance is proposed in this paper. The fault diagnosis consists of a dynamic simulation model, a diagnostic scheme, and an optimization algorithm. A high-accuracy, nonlinear, dynamic gas turbine model using a modular modeling method is presented that involves thermophysical properties, a component characteristic chart, and system inertial. The startup process is simulated using this model. The consistency between the simulation results and the field operation data shows the validity of the model and the advantages of transient accumulated deviation. In addition, a diagnostic scheme is designed to fulfill this process. Finally, cuckoo search is selected to solve the optimization problem in fault diagnosis. Comparative diagnostic results for a gas turbine before and after washing indicate the improved effectiveness and accuracy of the proposed method of using data from transient processes, compared with traditional methods using data from the steady state.

Data monitoring system of technical diagnosis system for EAST

International Nuclear Information System (INIS)

Qian Jing; Weng Peide; Chen Zhuomin; Wu Yu; Xi Weibin; Luo Jiarong

2010-01-01

Technical diagnosis system (TDS) is an important subsystem to monitor status parameters of EAST (experimental advanced superconducting tokamak). The upgraded TDS data monitoring system is comprised of management floor, monitoring floor and field floor.. Security protection, malfunction record and analysis are designed to make the system stable, robust and friendly. During the past EAST campaigns, the data monitoring system has been operated reliably and stably. The signal conditioning system and software architecture are described. (authors)

[Public health impact of a remote diagnosis system implemented in regional and district hospitals in Paraguay].

Science.gov (United States)

Galván, Pedro; Velázquez, Miguel; Benítez, Gualberto; Ortellado, José; Rivas, Ronald; Barrios, Antonio; Hilario, Enrique

2017-06-08

Determine the viability of a remote diagnosis system implemented to provide health care to remote and scattered populations in Paraguay. The study was conducted in all regional and general hospitals in Paraguay, and in the main district hospitals in the country's 18 health regions. Clinical data, tomographic images, sonography, and electrocardiograms (ECGs) of patients who needed a diagnosis by a specialized physician were entered into the system. This information was sent to specialists in diagnostic imaging and in cardiology for remote diagnosis and the report was then forwarded to the hospitals connected to the system. The cost-benefit and impact of the remote diagnosis tool was analyzed from the perspective of the National Health System. Between January 2014 and May 2015, a total of 34 096 remote diagnoses were made in 25 hospitals in the Ministry of Health's telemedicine system. The average unit cost of remote diagnosis was US$2.6 per ECG, tomography, and sonography, while the unit cost of "face-to-face" diagnosis was US$11.8 per ECG, US$68.6 per tomography, and US$21.5 per sonography. As a result of remote diagnosis, unit costs were 4.5 times lower for ECGs; 26.4 times lower for tomography, and 8.3 times lower for sonography. In monetary terms, implementation of the remote diagnosis system during the 16 months of the study led to average savings of US$2 420 037. Paraguay has a remote diagnosis system for electrocardiography, tomography, and sonography, using low-cost information and communications technologies (ICTs) based on free software that is scalable to other types of remote diagnostic studies of interest for public health. Implementation of remote diagnosis helped to strengthen the integrated network of health services and programs, enabling professionals to optimize their time and productivity, while improving quality, increasing access and equity, and reducing costs.

Learning-based diagnosis and repair

NARCIS (Netherlands)

Roos, Nico

2017-01-01

This paper proposes a new form of diagnosis and repair based on reinforcement learning. Self-interested agents learn locally which agents may provide a low quality of service for a task. The correctness of learned assessments of other agents is proved under conditions on exploration versus

Insulation diagnosis of rotating machines for elevators by an expert system based on fuzzy inference. Fuzzy suiron wo donyushita expert system ni yoru shokokiyo kaitenki no zetsuen shindan

Energy Technology Data Exchange (ETDEWEB)

Kaneko, K.; Oshima, H. (Tokai Univ., Tokyo (Japan)); Yamada, N.; Iijima, T. (Mitsubishi Electric Building Techno-Service Co. Ltd., Tokyo (Japan))

1992-11-20

Using the data measured with the insulation deterioration diagnostic system for rotating machines for elevators, which is newly developed utilizing the past experience, an expert system which enables insulation deterioration diagnosis even by field maintenance engineers to some extent. In this system, the knowledge and experience of specialists are loaded in a personal computer as the rule for insulation deterioration diagnosis to perform insulation deterioration diagnosis by fuzzy inference and 'hypothesis-verification' type backward reasoning inference. The structured expert system is outlined. The result of insulation diagnosis by this system s compared with that made by specialists to evaluate the effectiveness of the diagnosed result of this system, and shows 84% agreement with the results obtained by specialists. It is, therefore, considered to be a highly practical expert system. 10 refs., 7 figs., 1 tab.

failures diagnosis. Theory and practice for industrial systems

International Nuclear Information System (INIS)

Zwingelstein, G.

1995-01-01

Failure diagnosis methods represent appreciable tools for the maintenance and the improvement of availability and safety in complex industrial installations. The industrial diagnosis can be assimilated to a deterministic causality relation between the cause and the effect. This book describes the methodology associated to the resolution of the diagnosis problem applied to complex industrial system failures, and evaluates the principles of the main diagnosis methods. The introduction presents the terminology and norms used in the industry to situate the diagnosis context in the possession cost of a product. After a formulation of the diagnosis in the form of the resolution of inverse problems, the author gives details about the inductive and deductive methods and about internal and external diagnosis methods. Each method is illustrated with examples taken in the industry with recommendations about their operating limitations. Finally, a guideline summarizes the principal criteria for the selection of an industrial diagnosis method according to the available informations. (J.S.). 168 refs., 294 figs., 22 tabs., 1 annexe

Meckel-Gruber Syndrome: Autopsy Based Approach to Diagnosis

Directory of Open Access Journals (Sweden)

Asaranti Kar

2016-01-01

Full Text Available Meckel-Gruber syndrome (MGS is a rare lethal congenital malformation affecting 1 in 13,250-140,000 live births. The classical diagnostic triad comprises multicystic dysplastic kidneys, occipital encephalocele, and postaxial polydactyly. It can variably be associated with other malformations such as cleft lip and palate, pulmonary hypoplasia, hepatic fibrosis, and anomalies of central nervous system. A 20 weeks fetus was diagnosed as MGS with classical features along with many other congenital abnormalities such as microcephaly, microphthalmia, hypertelorism, cleft lip and palate, neonatal teeth, and the right side club foot which were detected only after doing autopsy. This case is reported because of its rarity emphasizing the importance of neonatal autopsy in every case of fetal death, especially where the antenatal diagnosis has not been made previously. A systematic approach to accurate diagnosis of MGS based on autopsy will be described here which can allow recurrence risk counseling and proper management in future pregnancies.

Intelligent Adaptation Process for Case Based Systems

International Nuclear Information System (INIS)

Nassar, A.M.; Mohamed, A.H.; Mohamed, A.H.

2014-01-01

Case Based Reasoning (CBR) Systems is one of the important decision making systems applied in many fields all over the world. The effectiveness of any CBR system based on the quality of the storage cases in the case library. Similar cases can be retrieved and adapted to produce the solution for the new problem. One of the main issues faced the CBR systems is the difficulties of achieving the useful cases. The proposed system introduces a new approach that uses the genetic algorithm (GA) technique to automate constructing the cases into the case library. Also, it can optimize the best one to be stored in the library for the future uses. However, the proposed system can avoid the problems of the uncertain and noisy cases. Besides, it can simply the retrieving and adaptation processes. So, it can improve the performance of the CBR system. The suggested system can be applied for many real-time problems. It has been applied for diagnosis the faults of the wireless network, diagnosis of the cancer diseases, diagnosis of the debugging of a software as cases of study. The proposed system has proved its performance in this field

From conventional software based systems to knowledge based systems

International Nuclear Information System (INIS)

Bologna, S.

1995-01-01

Even if todays nuclear power plants have a very good safety record, there is a continuous search for still improving safety. One direction of this effort address operational safety, trying to improve the handling of disturbances and accidents partly by further automation, partly by creating a better control room environment, providing the operator with intelligent support systems to help in the decision making process. Introduction of intelligent computerised operator support systems has proved to be an efficient way of improving the operators performance. A number of systems have been developed worldwide, assisting in tasks like process fault detection and diagnosis, selection and implementation of proper remedial actions. Unfortunately, the use of Knowledge Based Systems (KBSs), introduces a new dimension to the problem of the licensing process. KBSs, despite the different technology employed, are still nothing more than a computer program. Unfortunately, quite a few people building knowledge based systems seem to ignore the many good programming practices that have evolved over the years for producing traditional computer programs. In this paper the author will try to point out similarities and differences between conventional software based systems, and knowledge based systems, introducing also the concept of model based reasoning. (orig.) (25 refs., 2 figs.)

Information system for diagnosis of respiratory system diseases

Science.gov (United States)

Abramov, G. V.; Korobova, L. A.; Ivashin, A. L.; Matytsina, I. A.

2018-05-01

An information system is for the diagnosis of patients with lung diseases. The main problem solved by this system is the definition of the parameters of cough fragments in the monitoring recordings using a voice recorder. The authors give the recognition criteria of recorded cough moments, audio records analysis. The results of the research are systematized. The cough recognition system can be used by the medical specialists to diagnose the condition of the patients and to monitor the process of their treatment.

High yield of culture-based diagnosis in a TB-endemic setting

NARCIS (Netherlands)

Demers, Anne-Marie; Verver, Suzanne; Boulle, Andrew; Warren, Robin; van Helden, Paul; Behr, Marcel A.; Coetzee, David

2012-01-01

Background: In most of the world, microbiologic diagnosis of tuberculosis (TB) is limited to microscopy. Recent guidelines recommend culture-based diagnosis where feasible. Methods: In order to evaluate the relative and absolute incremental diagnostic yield of culture-based diagnosis in a

Development of expert system on personal computer for diagnosis of nuclear reactor malfunctions

International Nuclear Information System (INIS)

Kameyama, Takanori; Uekata, Tomomichi; Oka, Yoshiaki; Kondo, Shunsuke; Togo, Yasumasa

1988-01-01

An expert system on a personal computer has been developed for diagnosis of malfunction of the fast experimental reactor 'JOYO'. Prolog-KABA is used as the language. The system diagnoses the event which causes scram or set-back of the control rod after an alarm at steady state operation. The knowledge base (KB) consists of several sub-KBs and a meta-KB. Using the forward chaining, the meta-KB decides which sub-KB should be accessed. The cause of the malfunction is identified in the sub-KB using the backward chaining. The terms expressing the characteristics of the events are involved in the production rules as attributes in order to use the Prolog function of pattern matching and back-tracking for efficient inference. The total number of the rules in the system is about 400. The experiments using the plant simulator of 'JOYO' have shown that malfunctions are successfully identified by the diagnosis system. It takes about 10s for each diagnosis using the 16-bits personal computer, PC-9801 VM. (author)

Research on Fault Diagnosis Method Based on Rule Base Neural Network

Directory of Open Access Journals (Sweden)

Zheng Ni

2017-01-01

Full Text Available The relationship between fault phenomenon and fault cause is always nonlinear, which influences the accuracy of fault location. And neural network is effective in dealing with nonlinear problem. In order to improve the efficiency of uncertain fault diagnosis based on neural network, a neural network fault diagnosis method based on rule base is put forward. At first, the structure of BP neural network is built and the learning rule is given. Then, the rule base is built by fuzzy theory. An improved fuzzy neural construction model is designed, in which the calculated methods of node function and membership function are also given. Simulation results confirm the effectiveness of this method.

CT diagnosis of congenital anomalies of the central nervous system

International Nuclear Information System (INIS)

Mori, Koreaki

1980-01-01

In the diagnosis of central nervous system congenital anomalies, understanding of embryology of the central nervous system and pathophysiology of each anomaly are essential. It is important for clinical approach to central nervous system congenital anomalies to evaluate the size of the head and tention of the anterior fontanelle. Accurate diagnosis of congenital anomalies depends on a correlation of CT findings to clinical pictures. Clinical diagnosis of congenital anomalies should include prediction of treatability and prognosis, in addition to recognition of a disease. (author)

Product quality management based on CNC machine fault prognostics and diagnosis

Science.gov (United States)

Kozlov, A. M.; Al-jonid, Kh M.; Kozlov, A. A.; Antar, Sh D.

2018-03-01

This paper presents a new fault classification model and an integrated approach to fault diagnosis which involves the combination of ideas of Neuro-fuzzy Networks (NF), Dynamic Bayesian Networks (DBN) and Particle Filtering (PF) algorithm on a single platform. In the new model, faults are categorized in two aspects, namely first and second degree faults. First degree faults are instantaneous in nature, and second degree faults are evolutional and appear as a developing phenomenon which starts from the initial stage, goes through the development stage and finally ends at the mature stage. These categories of faults have a lifetime which is inversely proportional to a machine tool's life according to the modified version of Taylor’s equation. For fault diagnosis, this framework consists of two phases: the first one is focusing on fault prognosis, which is done online, and the second one is concerned with fault diagnosis which depends on both off-line and on-line modules. In the first phase, a neuro-fuzzy predictor is used to take a decision on whether to embark Conditional Based Maintenance (CBM) or fault diagnosis based on the severity of a fault. The second phase only comes into action when an evolving fault goes beyond a critical threshold limit called a CBM limit for a command to be issued for fault diagnosis. During this phase, DBN and PF techniques are used as an intelligent fault diagnosis system to determine the severity, time and location of the fault. The feasibility of this approach was tested in a simulation environment using the CNC machine as a case study and the results were studied and analyzed.

Distributed Cooperation Solution Method of Complex System Based on MAS

Science.gov (United States)

Weijin, Jiang; Yuhui, Xu

To adapt the model in reconfiguring fault diagnosing to dynamic environment and the needs of solving the tasks of complex system fully, the paper introduced multi-Agent and related technology to the complicated fault diagnosis, an integrated intelligent control system is studied in this paper. Based on the thought of the structure of diagnostic decision and hierarchy in modeling, based on multi-layer decomposition strategy of diagnosis task, a multi-agent synchronous diagnosis federation integrated different knowledge expression modes and inference mechanisms are presented, the functions of management agent, diagnosis agent and decision agent are analyzed, the organization and evolution of agents in the system are proposed, and the corresponding conflict resolution algorithm in given, Layered structure of abstract agent with public attributes is build. System architecture is realized based on MAS distributed layered blackboard. The real world application shows that the proposed control structure successfully solves the fault diagnose problem of the complex plant, and the special advantage in the distributed domain.

Evaluation of computer-aided detection and diagnosis systems.

Science.gov (United States)

Petrick, Nicholas; Sahiner, Berkman; Armato, Samuel G; Bert, Alberto; Correale, Loredana; Delsanto, Silvia; Freedman, Matthew T; Fryd, David; Gur, David; Hadjiiski, Lubomir; Huo, Zhimin; Jiang, Yulei; Morra, Lia; Paquerault, Sophie; Raykar, Vikas; Samuelson, Frank; Summers, Ronald M; Tourassi, Georgia; Yoshida, Hiroyuki; Zheng, Bin; Zhou, Chuan; Chan, Heang-Ping

2013-08-01

Computer-aided detection and diagnosis (CAD) systems are increasingly being used as an aid by clinicians for detection and interpretation of diseases. Computer-aided detection systems mark regions of an image that may reveal specific abnormalities and are used to alert clinicians to these regions during image interpretation. Computer-aided diagnosis systems provide an assessment of a disease using image-based information alone or in combination with other relevant diagnostic data and are used by clinicians as a decision support in developing their diagnoses. While CAD systems are commercially available, standardized approaches for evaluating and reporting their performance have not yet been fully formalized in the literature or in a standardization effort. This deficiency has led to difficulty in the comparison of CAD devices and in understanding how the reported performance might translate into clinical practice. To address these important issues, the American Association of Physicists in Medicine (AAPM) formed the Computer Aided Detection in Diagnostic Imaging Subcommittee (CADSC), in part, to develop recommendations on approaches for assessing CAD system performance. The purpose of this paper is to convey the opinions of the AAPM CADSC members and to stimulate the development of consensus approaches and "best practices" for evaluating CAD systems. Both the assessment of a standalone CAD system and the evaluation of the impact of CAD on end-users are discussed. It is hoped that awareness of these important evaluation elements and the CADSC recommendations will lead to further development of structured guidelines for CAD performance assessment. Proper assessment of CAD system performance is expected to increase the understanding of a CAD system's effectiveness and limitations, which is expected to stimulate further research and development efforts on CAD technologies, reduce problems due to improper use, and eventually improve the utility and efficacy of CAD in

Development research of expert system for diagnosis of landslide

Energy Technology Data Exchange (ETDEWEB)

Yoshikawa, Toru; Soeda, Yoshio; Nakamura, Hirohisa [Kansai Electric Power Co. Inc., Osaka (Japan)

1989-03-25

Measures against landslides are based upon a judgment to be made by combined application of professional knowledge of the scientific fields such as topography and geology, etc. and Kansai Electric Power Co. tried to construct a technical support system for preliminary diagnosis of landslide with which field engineers can easily utilize expert knowledge and to which artificial intelligence (AI) is applied. This system is to diagnose preliminarily the existence of such a landslide-prone area which is likely to hamper the project concerned at its early stage and after examination, those considered to be appropriate for the purpose were selected from among the artificial intelligence tools already developed. And as the knowledge base, knowledge was arranged in order with regard to the common features of landslide-prone areas, classification of landslide spots, landslide-prone topography and confusing topography, and procedures as well as remarks to be taken in reading the landslide topography, and was transformed as rule in order to input as the knowledge base into a computer. The system used the aerial photography interpretation theory as the base for its expert knowledge base and the materials necessary therefore were confined to easily obtainable aerial photographs and topographical maps. The system was prepared with a general purpose personal computer. 4 figs., 1 tab.

Computer Based Expert Systems.

Science.gov (United States)

Parry, James D.; Ferrara, Joseph M.

1985-01-01

Claims knowledge-based expert computer systems can meet needs of rural schools for affordable expert advice and support and will play an important role in the future of rural education. Describes potential applications in prediction, interpretation, diagnosis, remediation, planning, monitoring, and instruction. (NEC)

A fuzzy-logic based diagnosis and control of a reactor performing complete autotrophic nitrogen removal

DEFF Research Database (Denmark)

Mauricio Iglesias, Miguel; Vangsgaard, Anna Katrine; Gernaey, Krist

2013-01-01

Diagnosis and control modules based on fuzzy set theory were tested for novel bioreactor monitoring and control. Two independent modules were used jointly to carry out first the diagnosis of the state of the system and then use transfer this information to control the reactor. The separation in d...... autotrophic nitrogen removal process. The whole module is evaluated by dynamic simulation....

Knowledge-driven board-level functional fault diagnosis

CERN Document Server

Ye, Fangming; Chakrabarty, Krishnendu; Gu, Xinli

2017-01-01

This book provides a comprehensive set of characterization, prediction, optimization, evaluation, and evolution techniques for a diagnosis system for fault isolation in large electronic systems. Readers with a background in electronics design or system engineering can use this book as a reference to derive insightful knowledge from data analysis and use this knowledge as guidance for designing reasoning-based diagnosis systems. Moreover, readers with a background in statistics or data analytics can use this book as a practical case study for adapting data mining and machine learning techniques to electronic system design and diagnosis. This book identifies the key challenges in reasoning-based, board-level diagnosis system design and presents the solutions and corresponding results that have emerged from leading-edge research in this domain. It covers topics ranging from highly accurate fault isolation, adaptive fault isolation, diagnosis-system robustness assessment, to system performance analysis and evalua...

Application of learning techniques based on kernel methods for the fault diagnosis in industrial processes

Directory of Open Access Journals (Sweden)

Jose M. Bernal-de-Lázaro

2016-05-01

Full Text Available This article summarizes the main contributions of the PhD thesis titled: "Application of learning techniques based on kernel methods for the fault diagnosis in Industrial processes". This thesis focuses on the analysis and design of fault diagnosis systems (DDF based on historical data. Specifically this thesis provides: (1 new criteria for adjustment of the kernel methods used to select features with a high discriminative capacity for the fault diagnosis tasks, (2 a proposed approach process monitoring using statistical techniques multivariate that incorporates a reinforced information concerning to the dynamics of the Hotelling's T2 and SPE statistics, whose combination with kernel methods improves the detection of small-magnitude faults; (3 an robustness index to compare the diagnosis classifiers performance taking into account their insensitivity to possible noise and disturbance on historical data.

A Web-Based Rice Plant Expert System Using Rule-Based Reasoning

Directory of Open Access Journals (Sweden)

Anton Setiawan Honggowibowo

2009-12-01

Full Text Available Rice plants can be attacked by various kinds of diseases which are possible to be determined from their symptoms. However, it is to recognize that to find out the exact type of disease, an agricultural expert’s opinion is needed, meanwhile the numbers of agricultural experts are limited and there are too many problems to be solved at the same time. This makes a system with a capability as an expert is required. This system must contain the knowledge of the diseases and symptom of rice plants as an agricultural expert has to have. This research designs a web-based expert system using rule-based reasoning. The rule are modified from the method of forward chaining inference and backward chaining in order to to help farmers in the rice plant disease diagnosis. The web-based rice plants disease diagnosis expert system has the advantages to access and use easily. With web-based features inside, it is expected that the farmer can accesse the expert system everywhere to overcome the problem to diagnose rice diseases.

Reasoning based in cases applied to diagnosis of electric generators; Razonamiento basado en casos aplicado al diagnostico de generadores electricos

Energy Technology Data Exchange (ETDEWEB)

De la Torre Vega, H. Octavio; Garcia Tevillo, Arturo; Campuzano Martinez, Roberto [Instituto de Investigaciones Electricas, Temixco, Morelos (Mexico); Lopez Azamar, Ernesto [Comision Federal de Electricidad (Mexico)

2000-07-01

The development of a system for the diagnosis of electrical generators that apply techniques of artificial intelligence, is presented, as it is the reasoning based on cases, to support the work of the diagnosis engineer. This system is part of a system called CADIS, dedicated to the diagnosis of electrical generators out of line and reason of previous articles. In this occasion the characteristics of the reasoning module based on experiences (SirBE) are emphasized, indicating how to make a diagnosis using similar cases and how to edit the system base of experience, using the interactive editor of cases. It is included, in addition, a summarized example which represents a case for SirBE and how the system helps to make a diagnosis. [Spanish] Se presenta el desarrollo de un sistema de diagnostico de generadores electricos que aplica tecnicas de inteligencia artificial, como es el razonamiento basado en casos, para apoyar la labor del ingeniero de diagnostico. Este sistema es parte de un sistema denominado CADIS, dedicado al diagnostico de generadores electricos fuera de linea y motivo de articulos anteriores. En esta ocasion se resaltan las caracteristicas del modulo de razonamiento basado en experiencias (SirBE), indicando como realizar un diagnostico utilizando casos similares y como editar la base de experiencia del sistema utilizando el editor interactivo de casos. Se incluye, ademas, un ejemplo resumido de lo que representa un caso para SiRBE y como el sistema ayuda a realizar un diagnostico.

Model-Based Fault Diagnosis Techniques Design Schemes, Algorithms and Tools

CERN Document Server

Ding, Steven X

2013-01-01

Guaranteeing a high system performance over a wide operating range is an important issue surrounding the design of automatic control systems with successively increasing complexity. As a key technology in the search for a solution, advanced fault detection and identification (FDI) is receiving considerable attention. This book introduces basic model-based FDI schemes, advanced analysis and design algorithms, and mathematical and control-theoretic tools. This second edition of Model-Based Fault Diagnosis Techniques contains: ·         new material on fault isolation and identification, and fault detection in feedback control loops; ·         extended and revised treatment of systematic threshold determination for systems with both deterministic unknown inputs and stochastic noises; addition of the continuously-stirred tank heater as a representative process-industrial benchmark; and ·         enhanced discussion of residual evaluation in stochastic processes. Model-based Fault Diagno...

Funding intensive care - approaches in systems using diagnosis-related groups.

OpenAIRE

Ettelt, S; Nolte, E

2010-01-01

This report reviews approaches to funding intensive care in health systems that use activitybased payment mechanisms based on diagnosis-related groups (DRGs) to reimburse hospital care. The report aims to inform the current debate about options for funding intensive care services for adults, children and newborns in England. Funding mechanisms reviewed here include those in Australia (Victoria), Denmark, France, Germany, Italy, Spain, Sweden and the United States (Medicare). Approaches to org...

Decision tree and PCA-based fault diagnosis of rotating machinery

Science.gov (United States)

Sun, Weixiang; Chen, Jin; Li, Jiaqing

2007-04-01

After analysing the flaws of conventional fault diagnosis methods, data mining technology is introduced to fault diagnosis field, and a new method based on C4.5 decision tree and principal component analysis (PCA) is proposed. In this method, PCA is used to reduce features after data collection, preprocessing and feature extraction. Then, C4.5 is trained by using the samples to generate a decision tree model with diagnosis knowledge. At last the tree model is used to make diagnosis analysis. To validate the method proposed, six kinds of running states (normal or without any defect, unbalance, rotor radial rub, oil whirl, shaft crack and a simultaneous state of unbalance and radial rub), are simulated on Bently Rotor Kit RK4 to test C4.5 and PCA-based method and back-propagation neural network (BPNN). The result shows that C4.5 and PCA-based diagnosis method has higher accuracy and needs less training time than BPNN.

An Event-based Distributed Diagnosis Framework using Structural Model Decomposition

Data.gov (United States)

National Aeronautics and Space Administration — Complex engineering systems require efficient on-line fault diagnosis methodologies to improve safety and reduce maintenance costs. Traditionally, diagnosis...

A statistical-based approach for fault detection and diagnosis in a photovoltaic system

KAUST Repository

Garoudja, Elyes

2017-07-10

This paper reports a development of a statistical approach for fault detection and diagnosis in a PV system. Specifically, the overarching goal of this work is to early detect and identify faults on the DC side of a PV system (e.g., short-circuit faults; open-circuit faults; and partial shading faults). Towards this end, we apply exponentially-weighted moving average (EWMA) control chart on the residuals obtained from the one-diode model. Such a choice is motivated by the greater sensitivity of EWMA chart to incipient faults and its low-computational cost making it easy to implement in real time. Practical data from a 3.2 KWp photovoltaic plant located within an Algerian research center is used to validate the proposed approach. Results show clearly the efficiency of the developed method in monitoring PV system status.

Computer-Aided Diagnosis Systems for Brain Diseases in Magnetic Resonance Images

Directory of Open Access Journals (Sweden)

Yasuo Yamashita

2009-07-01

Full Text Available This paper reviews the basics and recent researches of computer-aided diagnosis (CAD systems for assisting neuroradiologists in detection of brain diseases, e.g., asymptomatic unruptured aneurysms, Alzheimer's disease, vascular dementia, and multiple sclerosis (MS, in magnetic resonance (MR images. The CAD systems consist of image feature extraction based on image processing techniques and machine learning classifiers such as linear discriminant analysis, artificial neural networks, and support vector machines. We introduce useful examples of the CAD systems in the neuroradiology, and conclude with possibilities in the future of the CAD systems for brain diseases in MR images.

Fault diagnosis in spur gears based on genetic algorithm and random forest

Science.gov (United States)

Cerrada, Mariela; Zurita, Grover; Cabrera, Diego; Sánchez, René-Vinicio; Artés, Mariano; Li, Chuan

2016-03-01

There are growing demands for condition-based monitoring of gearboxes, and therefore new methods to improve the reliability, effectiveness, accuracy of the gear fault detection ought to be evaluated. Feature selection is still an important aspect in machine learning-based diagnosis in order to reach good performance of the diagnostic models. On the other hand, random forest classifiers are suitable models in industrial environments where large data-samples are not usually available for training such diagnostic models. The main aim of this research is to build up a robust system for the multi-class fault diagnosis in spur gears, by selecting the best set of condition parameters on time, frequency and time-frequency domains, which are extracted from vibration signals. The diagnostic system is performed by using genetic algorithms and a classifier based on random forest, in a supervised environment. The original set of condition parameters is reduced around 66% regarding the initial size by using genetic algorithms, and still get an acceptable classification precision over 97%. The approach is tested on real vibration signals by considering several fault classes, one of them being an incipient fault, under different running conditions of load and velocity.

A real-time expert system for nuclear power plant failure diagnosis and operational guide

International Nuclear Information System (INIS)

Naito, N.; Sakuma, A.; Shigeno, K.; Mori, N.

1987-01-01

A real-time expert system (DIAREX) has been developed to diagnose plant failure and to offer a corrective operational guide for boiling water reactor (BWR) power plants. The failure diagnosis model used in DIAREX was systematically developed, based mainly on deep knowledge, to cover heuristics. Complex paradigms for knowledge representation were adopted, i.e., the process representation language and the failure propagation tree. The system is composed of a knowledge base, knowledge base editor, preprocessor, diagnosis processor, and display processor. The DIAREX simulation test has been carried out for many transient scenarios, including multiple failures, using a real-time full-scope simulator modeled after the 1100-MW(electric) BWR power plant. Test results showed that DIAREX was capable of diagnosing a plant failure quickly and of providing a corrective operational guide with a response time fast enough to offer valuable information to plant operators

Consultation system for image diagnosis: Report formation support system

International Nuclear Information System (INIS)

Ikeda, M.; Sakuma, S.; Ishigaki, T.; Suzuki, K.; Oikawa, K.

1987-01-01

The authors developed a consultation system for image diagnosis, involving artificial intelligence ideas. In this system, the authors proposed a new report formation support system and implemented it in lymphangiography. This support system starts with the input of image interpretation. The input process is made mainly by selecting items. This system encodes the input findings into the semantic network, which is represented as a directed graph, and it reserves them into the knowledge database in the above structure. Finally, the output (report) is made in the near natural language, which corresponds to the input findings

Fault diagnosis of an intelligent hydraulic pump based on a nonlinear unknown input observer

Directory of Open Access Journals (Sweden)

Zhonghai MA

2018-02-01

Full Text Available Hydraulic piston pumps are commonly used in aircraft. In order to improve the viability of aircraft and energy efficiency, intelligent variable pressure pump systems have been used in aircraft hydraulic systems more and more widely. Efficient fault diagnosis plays an important role in improving the reliability and performance of hydraulic systems. In this paper, a fault diagnosis method of an intelligent hydraulic pump system (IHPS based on a nonlinear unknown input observer (NUIO is proposed. Different from factors of a full-order Luenberger-type unknown input observer, nonlinear factors of the IHPS are considered in the NUIO. Firstly, a new type of intelligent pump is presented, the mathematical model of which is established to describe the IHPS. Taking into account the real-time requirements of the IHPS and the special structure of the pump, the mechanism of the intelligent pump and failure modes are analyzed and two typical failure modes are obtained. Furthermore, a NUIO of the IHPS is performed based on the output pressure and swashplate angle signals. With the residual error signals produced by the NUIO, online intelligent pump failure occurring in real-time can be detected. Lastly, through analysis and simulation, it is confirmed that this diagnostic method could accurately diagnose and isolate those typical failure modes of the nonlinear IHPS. The method proposed in this paper is of great significance in improving the reliability of the IHPS. Keywords: Fault diagnosis, Hydraulic piston pump, Model-based, Nonlinear unknown input observer (NUIO, Residual error

Expert system for surveillance and diagnosis of breach fuel elements

Science.gov (United States)

Gross, Kenny C.

1989-01-01

An apparatus and method are disclosed for surveillance and diagnosis of breached fuel elements in a nuclear reactor. A delayed neutron monitoring system provides output signals indicating the delayed neutron activity and age and the equivalent recoil areas of a breached fuel element. Sensors are used to provide outputs indicating the status of each component of the delayed neutron monitoring system. Detectors also generate output signals indicating the reactor power level and the primary coolant flow rate of the reactor. The outputs from the detectors and sensors are interfaced with an artificial intelligence-based knowledge system which implements predetermined logic and generates output signals indicating the operability of the reactor.

Expert system for surveillance and diagnosis of breach fuel elements

International Nuclear Information System (INIS)

Gross, K.C.

1989-01-01

An apparatus and method are disclosed for surveillance and diagnosis of breached fuel elements in a nuclear reactor. A delayed neutron monitoring system provides output signals indicating the delayed neutron activity and age and the equivalent recoil areas of a breached fuel element. Sensors are used to provide outputs indicating the status of each component of the delayed neutron monitoring system. Detectors also generate output signals indicating the reactor power level and the primary coolant flow rate of the reactor. The outputs from the detectors and sensors are interfaced with an artificial intelligence-based knowledge system which implements predetermined logic and generates output signals indicating the operability of the reactor

FEATURE EXTRACTION BASED WAVELET TRANSFORM IN BREAST CANCER DIAGNOSIS USING FUZZY AND NON-FUZZY CLASSIFICATION

Directory of Open Access Journals (Sweden)

Pelin GORGEL

2013-01-01

Full Text Available This study helps to provide a second eye to the expert radiologists for the classification of manually extracted breast masses taken from 60 digital mammıgrams. These mammograms have been acquired from Istanbul University Faculty of Medicine Hospital and have 78 masses. The diagnosis is implemented with pre-processing by using feature extraction based Fast Wavelet Transform (FWT. Afterwards Adaptive Neuro-Fuzzy Inference System (ANFIS based fuzzy subtractive clustering and Support Vector Machines (SVM methods are used for the classification. It is a comparative study which uses these methods respectively. According to the results of the study, ANFIS based subtractive clustering produces ??% while SVM produces ??% accuracy in malignant-benign classification. The results demonstrate that the developed system could help the radiologists for a true diagnosis and decrease the number of the missing cancerous regions or unnecessary biopsies.

Automatic fault diagnosis in PV systems with distributed MPPT

International Nuclear Information System (INIS)

Solórzano, J.; Egido, M.A.

2013-01-01

Highlights: • An automatic failure diagnosis procedure for PV systems with DMPPT is presented. • The different failures diagnosed and their effects on the PV systems are described. • No use of irradiance and temperature sensors decreasing the cost of the system. • Voltage and current analysis to diagnose different failures. • Hot-spots, localized dirt, shading, module degradation and cable losses diagnosis. - Abstract: This work presents a novel procedure for fault diagnosis in PV systems with distributed maximum power point tracking at module level—power optimizers (DC/DC) or micro-inverters (DC/AC). Apart from the power benefits obtained when an irregular irradiance distribution is present, this type of systems permit the monitoring of the PV plant parameters at the module level: voltage and current at the working power point. With these parameters, a prototype diagnosis tool has been developed in Matlab and it has been experimentally verified in a real rooftop PV generator by applying different failures. The tool can diagnose the following failures: fixed object shading (with distance estimation), localized dirt, generalized dirt, possible hot-spots, module degradation and excessive losses in DC cables. In addition, it alerts the user of the power losses produced by each failure and classifies the failures by their severity. This system does not require the use of irradiance or temperature sensors, except for the generalized dirt failure, reducing the cost of installation, especially important in small PV systems

Expert system for the diagnosis of the condition and performance of centrifugal pumps

Energy Technology Data Exchange (ETDEWEB)

Jantunen, E; Vaehae-Pietilae, K; Pesonen, K [Technical Research Centre of Finland, Manufacturing Technology, Espoo (Finland)

1998-12-31

A brief description of the results of a study concerning the maintenance and downtime costs in Finnish pumping is given. The leakage of seals was found to be the fault that causes the highest downtime and maintenance costs. A small laboratory arrangement has been used to test the effectiveness of various condition monitoring methods. This information has been used in the development of a diagnostic expert system called CEPDIA, which can be used for diagnosing the condition of a pump and its components. The diagnosis is based on measuring results obtained from sensors and on information about maintenance actions carried out with the pump and its components. The principles of the CEPDIA expert system are described. A database is included in the system for handling and saving the measurement results, technical information on the pumps and maintenance actions carried out with the pumps. The diagnosis can also be based on vibration signature analysis, which is quite effective in determining which fault is the actual cause of malfunction of the pump or its components. CEPDIA can also be used to calculate of the efficiency of the electrical motor and the pump. CEPDIA has been tested in the diagnosis of 63 pumps. The average efficiency in pumping was less than 40 %, and more than 10 % of the pumps were pumping with less than 10 % efficiency. (orig.) 11 refs.

Expert system for the diagnosis of the condition and performance of centrifugal pumps

Energy Technology Data Exchange (ETDEWEB)

Jantunen, E.; Vaehae-Pietilae, K.; Pesonen, K. [Technical Research Centre of Finland, Manufacturing Technology, Espoo (Finland)

1997-12-31

A brief description of the results of a study concerning the maintenance and downtime costs in Finnish pumping is given. The leakage of seals was found to be the fault that causes the highest downtime and maintenance costs. A small laboratory arrangement has been used to test the effectiveness of various condition monitoring methods. This information has been used in the development of a diagnostic expert system called CEPDIA, which can be used for diagnosing the condition of a pump and its components. The diagnosis is based on measuring results obtained from sensors and on information about maintenance actions carried out with the pump and its components. The principles of the CEPDIA expert system are described. A database is included in the system for handling and saving the measurement results, technical information on the pumps and maintenance actions carried out with the pumps. The diagnosis can also be based on vibration signature analysis, which is quite effective in determining which fault is the actual cause of malfunction of the pump or its components. CEPDIA can also be used to calculate of the efficiency of the electrical motor and the pump. CEPDIA has been tested in the diagnosis of 63 pumps. The average efficiency in pumping was less than 40 %, and more than 10 % of the pumps were pumping with less than 10 % efficiency. (orig.) 11 refs.

Fault Diagnosis System of Induction Motors Based on Neural Network and Genetic Algorithm Using Stator Current Signals

Directory of Open Access Journals (Sweden)

Tian Han

2006-01-01

Full Text Available This paper proposes an online fault diagnosis system for induction motors through the combination of discrete wavelet transform (DWT, feature extraction, genetic algorithm (GA, and neural network (ANN techniques. The wavelet transform improves the signal-to-noise ratio during a preprocessing. Features are extracted from motor stator current, while reducing data transfers and making online application available. GA is used to select the most significant features from the whole feature database and optimize the ANN structure parameter. Optimized ANN is trained and tested by the selected features of the measurement data of stator current. The combination of advanced techniques reduces the learning time and increases the diagnosis accuracy. The efficiency of the proposed system is demonstrated through motor faults of electrical and mechanical origins on the induction motors. The results of the test indicate that the proposed system is promising for the real-time application.

Remote diagnosis as used for mechanized parking systems

Science.gov (United States)

Humberg, Heinz; Maeder, Hans Friedrich; Will, Frank

1992-10-01

The new possibilities offered by worldwide data transmission networks, which are being used for the remote diagnosis of mechanized parking systems are discussed. This has led to a reduction in service costs for systems installed in Asia and elsewhere. The principles of the mechanized multistorey car park and their control concept are described. The parking facilities are fully geared up for remote diagnosis, the key functions of which are: data collection, data storage, data transmission, and data evaluation. The reports transmitted from the parking facility are analyzed using an evaluation system. The objectives are to detect impending component failures and to quickly identify the causes of irregularities which have occurred. The evaluation system can be easily adapted for other applications.

Diagnosis of common hemoglobinopathies among South East Asian population using capillary isoelectric focusing system.

Science.gov (United States)

Srivorakun, H; Fucharoen, G; Sanchaisuriya, K; Fucharoen, S

2017-02-01

We have evaluated an automated capillary isoelectric focusing (cIEF)-based Hb analyzer in diagnosis of hemoglobinopathies commonly found among South East Asian population. Study was performed on a cohort of 665 adult Thai subjects and 13 fetal blood specimens obtained at routine thalassemia diagnostic laboratory. Hb analysis was performed using the cIEF system. Thalassemia genotypes were defined by DNA analysis. The system revealed satisfactorily within-run and between-run precision for quantitation of Hb A 2 and Hb E (CV: 0.02-0.09%). The reference ranges of Hb A 2 and Hb E were 2.6-4.0% and 25.7-33.1%, respectively. The system identified the cases of β-thalassemia and Hb E disorders correctly. Several thalassemia genotypes and Hb variants were identifiable. However, Hb Constant Spring was separated closely to Hb A 2 and Hbs Bart's and H were relatively difficult to be reported due to interfering peaks separating at the same regions. Prenatal diagnosis by fetal blood analysis was found to be accurate for Hb Bart's hydrops fetalis and Hb E-β 0 -thalassemia disease. The cIEF system could accurately diagnose β-thalassemia and Hb E carriers and demonstrate many Hb variants found in the region. The system cannot report Hb A 2 in the presence of Hb E whereas Hbs Lepore and F are comigrated. Diagnosis of α-thalassemia disease based on Hb H and Hb Bart's might be difficult. © 2016 John Wiley & Sons Ltd.

Active fault diagnosis in closed-loop systems

DEFF Research Database (Denmark)

Niemann, Hans Henrik; Poulsen, Niels Kjølstad

2005-01-01

Active fault diagnosis (AFD) of parametric faults is considered in connection with closed loop feedback systems. AFD involves auxiliary signals applied on the closed loop system. A fault signature matrix is introduced in connection with AFD and it is shown that if a limited number of faults can...

Water quality diagnosis system

International Nuclear Information System (INIS)

Nagase, Makoto; Asakura, Yamato; Sakagami, Masaharu

1989-01-01

By using a model representing a relationship between the water quality parameter and the dose rate in primary coolant circuits of a water cooled reactor, forecasting for the feature dose rate and abnormality diagnosis for the water quality are conducted. The analysis model for forecasting the reactor water activity or the dose rate receives, as the input, estimated curves for the forecast Fe, Ni, Co concentration in feedwater or reactor water pH, etc. from the water quality data in the post and forecasts the future radioactivity or dose rate in the reactor water. By comparing the result of the forecast and the setting value such as an aimed value, it can be seen whether the water quality at present or estimated to be changed is satisfactory or not. If the quality is not satisfactory, it is possible to take an early countermeasure. Accordingly, the reactor water activity and the dose rate can be kept low. Further, the basic system constitution, diagnosis algorithm, indication, etc. are identical between BWR and PWR reactors, except for only the difference in the mass balance. (K.M.)

Design of a modified adaptive neuro fuzzy inference system classifier for medical diagnosis of Pima Indians Diabetes

Science.gov (United States)

Sagir, Abdu Masanawa; Sathasivam, Saratha

2017-08-01

Medical diagnosis is the process of determining which disease or medical condition explains a person's determinable signs and symptoms. Diagnosis of most of the diseases is very expensive as many tests are required for predictions. This paper aims to introduce an improved hybrid approach for training the adaptive network based fuzzy inference system with Modified Levenberg-Marquardt algorithm using analytical derivation scheme for computation of Jacobian matrix. The goal is to investigate how certain diseases are affected by patient's characteristics and measurement such as abnormalities or a decision about presence or absence of a disease. To achieve an accurate diagnosis at this complex stage of symptom analysis, the physician may need efficient diagnosis system to classify and predict patient condition by using an adaptive neuro fuzzy inference system (ANFIS) pre-processed by grid partitioning. The proposed hybridised intelligent system was tested with Pima Indian Diabetes dataset obtained from the University of California at Irvine's (UCI) machine learning repository. The proposed method's performance was evaluated based on training and test datasets. In addition, an attempt was done to specify the effectiveness of the performance measuring total accuracy, sensitivity and specificity. In comparison, the proposed method achieves superior performance when compared to conventional ANFIS based gradient descent algorithm and some related existing methods. The software used for the implementation is MATLAB R2014a (version 8.3) and executed in PC Intel Pentium IV E7400 processor with 2.80 GHz speed and 2.0 GB of RAM.

An analysis of multi-agent diagnosis

NARCIS (Netherlands)

Roos, Nico; Ten Teije, Annette; Bos, André; Witteveen, Cees; Castelfranchi, C.; Johnson, W.L.

2002-01-01

This paper analyzes the use of a Multi-Agent System for Model-Based Diagnosis. In a large dynamical system, it is often infeasible or even impossible to maintain a model of the whole system. Instead, several incomplete models of the system have to be used to establish a diagnosis and to detect

Portable multispectral imaging system for oral cancer diagnosis

Science.gov (United States)

Hsieh, Yao-Fang; Ou-Yang, Mang; Lee, Cheng-Chung

2013-09-01

This study presents the portable multispectral imaging system that can acquire the image of specific spectrum in vivo for oral cancer diagnosis. According to the research literature, the autofluorescence of cells and tissue have been widely applied to diagnose oral cancer. The spectral distribution is difference for lesions of epithelial cells and normal cells after excited fluorescence. We have been developed the hyperspectral and multispectral techniques for oral cancer diagnosis in three generations. This research is the third generation. The excited and emission spectrum for the diagnosis are acquired from the research of first generation. The portable system for detection of oral cancer is modified for existing handheld microscope. The UV LED is used to illuminate the surface of oral cavity and excite the cells to produce fluorescent. The image passes through the central channel and filters out unwanted spectrum by the selection of filter, and focused by the focus lens on the image sensor. Therefore, we can achieve the specific wavelength image via fluorescence reaction. The specificity and sensitivity of the system are 85% and 90%, respectively.

Network Fault Diagnosis Using DSM

Institute of Scientific and Technical Information of China (English)

Jiang Hao; Yan Pu-liu; Chen Xiao; Wu Jing

2004-01-01

Difference similitude matrix (DSM) is effective in reducing information system with its higher reduction rate and higher validity. We use DSM method to analyze the fault data of computer networks and obtain the fault diagnosis rules. Through discretizing the relative value of fault data, we get the information system of the fault data. DSM method reduces the information system and gets the diagnosis rules. The simulation with the actual scenario shows that the fault diagnosis based on DSM can obtain few and effective rules.

Fault Diagnosis Method Based on Information Entropy and Relative Principal Component Analysis

Directory of Open Access Journals (Sweden)

Xiaoming Xu

2017-01-01

Full Text Available In traditional principle component analysis (PCA, because of the neglect of the dimensions influence between different variables in the system, the selected principal components (PCs often fail to be representative. While the relative transformation PCA is able to solve the above problem, it is not easy to calculate the weight for each characteristic variable. In order to solve it, this paper proposes a kind of fault diagnosis method based on information entropy and Relative Principle Component Analysis. Firstly, the algorithm calculates the information entropy for each characteristic variable in the original dataset based on the information gain algorithm. Secondly, it standardizes every variable’s dimension in the dataset. And, then, according to the information entropy, it allocates the weight for each standardized characteristic variable. Finally, it utilizes the relative-principal-components model established for fault diagnosis. Furthermore, the simulation experiments based on Tennessee Eastman process and Wine datasets demonstrate the feasibility and effectiveness of the new method.

Neural network-based robust actuator fault diagnosis for a non-linear multi-tank system.

Science.gov (United States)

Mrugalski, Marcin; Luzar, Marcel; Pazera, Marcin; Witczak, Marcin; Aubrun, Christophe

2016-03-01

The paper is devoted to the problem of the robust actuator fault diagnosis of the dynamic non-linear systems. In the proposed method, it is assumed that the diagnosed system can be modelled by the recurrent neural network, which can be transformed into the linear parameter varying form. Such a system description allows developing the designing scheme of the robust unknown input observer within H∞ framework for a class of non-linear systems. The proposed approach is designed in such a way that a prescribed disturbance attenuation level is achieved with respect to the actuator fault estimation error, while guaranteeing the convergence of the observer. The application of the robust unknown input observer enables actuator fault estimation, which allows applying the developed approach to the fault tolerant control tasks. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Computer aided diagnosis based on medical image processing and artificial intelligence methods

Science.gov (United States)

Stoitsis, John; Valavanis, Ioannis; Mougiakakou, Stavroula G.; Golemati, Spyretta; Nikita, Alexandra; Nikita, Konstantina S.

2006-12-01

Advances in imaging technology and computer science have greatly enhanced interpretation of medical images, and contributed to early diagnosis. The typical architecture of a Computer Aided Diagnosis (CAD) system includes image pre-processing, definition of region(s) of interest, features extraction and selection, and classification. In this paper, the principles of CAD systems design and development are demonstrated by means of two examples. The first one focuses on the differentiation between symptomatic and asymptomatic carotid atheromatous plaques. For each plaque, a vector of texture and motion features was estimated, which was then reduced to the most robust ones by means of ANalysis of VAriance (ANOVA). Using fuzzy c-means, the features were then clustered into two classes. Clustering performances of 74%, 79%, and 84% were achieved for texture only, motion only, and combinations of texture and motion features, respectively. The second CAD system presented in this paper supports the diagnosis of focal liver lesions and is able to characterize liver tissue from Computed Tomography (CT) images as normal, hepatic cyst, hemangioma, and hepatocellular carcinoma. Five texture feature sets were extracted for each lesion, while a genetic algorithm based feature selection method was applied to identify the most robust features. The selected feature set was fed into an ensemble of neural network classifiers. The achieved classification performance was 100%, 93.75% and 90.63% in the training, validation and testing set, respectively. It is concluded that computerized analysis of medical images in combination with artificial intelligence can be used in clinical practice and may contribute to more efficient diagnosis.

Computer aided diagnosis based on medical image processing and artificial intelligence methods

International Nuclear Information System (INIS)

Stoitsis, John; Valavanis, Ioannis; Mougiakakou, Stavroula G.; Golemati, Spyretta; Nikita, Alexandra; Nikita, Konstantina S.

2006-01-01

Advances in imaging technology and computer science have greatly enhanced interpretation of medical images, and contributed to early diagnosis. The typical architecture of a Computer Aided Diagnosis (CAD) system includes image pre-processing, definition of region(s) of interest, features extraction and selection, and classification. In this paper, the principles of CAD systems design and development are demonstrated by means of two examples. The first one focuses on the differentiation between symptomatic and asymptomatic carotid atheromatous plaques. For each plaque, a vector of texture and motion features was estimated, which was then reduced to the most robust ones by means of ANalysis of VAriance (ANOVA). Using fuzzy c-means, the features were then clustered into two classes. Clustering performances of 74%, 79%, and 84% were achieved for texture only, motion only, and combinations of texture and motion features, respectively. The second CAD system presented in this paper supports the diagnosis of focal liver lesions and is able to characterize liver tissue from Computed Tomography (CT) images as normal, hepatic cyst, hemangioma, and hepatocellular carcinoma. Five texture feature sets were extracted for each lesion, while a genetic algorithm based feature selection method was applied to identify the most robust features. The selected feature set was fed into an ensemble of neural network classifiers. The achieved classification performance was 100%, 93.75% and 90.63% in the training, validation and testing set, respectively. It is concluded that computerized analysis of medical images in combination with artificial intelligence can be used in clinical practice and may contribute to more efficient diagnosis

Computer aided diagnosis based on medical image processing and artificial intelligence methods

Energy Technology Data Exchange (ETDEWEB)

Stoitsis, John [National Technical University of Athens, School of Electrical and Computer Engineering, Athens 157 71 (Greece)]. E-mail: stoitsis@biosim.ntua.gr; Valavanis, Ioannis [National Technical University of Athens, School of Electrical and Computer Engineering, Athens 157 71 (Greece); Mougiakakou, Stavroula G. [National Technical University of Athens, School of Electrical and Computer Engineering, Athens 157 71 (Greece); Golemati, Spyretta [National Technical University of Athens, School of Electrical and Computer Engineering, Athens 157 71 (Greece); Nikita, Alexandra [University of Athens, Medical School 152 28 Athens (Greece); Nikita, Konstantina S. [National Technical University of Athens, School of Electrical and Computer Engineering, Athens 157 71 (Greece)

2006-12-20

Advances in imaging technology and computer science have greatly enhanced interpretation of medical images, and contributed to early diagnosis. The typical architecture of a Computer Aided Diagnosis (CAD) system includes image pre-processing, definition of region(s) of interest, features extraction and selection, and classification. In this paper, the principles of CAD systems design and development are demonstrated by means of two examples. The first one focuses on the differentiation between symptomatic and asymptomatic carotid atheromatous plaques. For each plaque, a vector of texture and motion features was estimated, which was then reduced to the most robust ones by means of ANalysis of VAriance (ANOVA). Using fuzzy c-means, the features were then clustered into two classes. Clustering performances of 74%, 79%, and 84% were achieved for texture only, motion only, and combinations of texture and motion features, respectively. The second CAD system presented in this paper supports the diagnosis of focal liver lesions and is able to characterize liver tissue from Computed Tomography (CT) images as normal, hepatic cyst, hemangioma, and hepatocellular carcinoma. Five texture feature sets were extracted for each lesion, while a genetic algorithm based feature selection method was applied to identify the most robust features. The selected feature set was fed into an ensemble of neural network classifiers. The achieved classification performance was 100%, 93.75% and 90.63% in the training, validation and testing set, respectively. It is concluded that computerized analysis of medical images in combination with artificial intelligence can be used in clinical practice and may contribute to more efficient diagnosis.

Diagnosis of multiple system atrophy.

Science.gov (United States)

Palma, Jose-Alberto; Norcliffe-Kaufmann, Lucy; Kaufmann, Horacio

2018-05-01

Multiple system atrophy (MSA) may be difficult to distinguish clinically from other disorders, particularly in the early stages of the disease. An autonomic-only presentation can be indistinguishable from pure autonomic failure. Patients presenting with parkinsonism may be misdiagnosed as having Parkinson disease. Patients presenting with the cerebellar phenotype of MSA can mimic other adult-onset ataxias due to alcohol, chemotherapeutic agents, lead, lithium, and toluene, or vitamin E deficiency, as well as paraneoplastic, autoimmune, or genetic ataxias. A careful medical history and meticulous neurological examination remain the cornerstone for the accurate diagnosis of MSA. Ancillary investigations are helpful to support the diagnosis, rule out potential mimics, and define therapeutic strategies. This review summarizes diagnostic investigations useful in the differential diagnosis of patients with suspected MSA. Currently used techniques include structural and functional brain imaging, cardiac sympathetic imaging, cardiovascular autonomic testing, olfactory testing, sleep study, urological evaluation, and dysphagia and cognitive assessments. Despite advances in the diagnostic tools for MSA in recent years and the availability of consensus criteria for clinical diagnosis, the diagnostic accuracy of MSA remains sub-optimal. As other diagnostic tools emerge, including skin biopsy, retinal biomarkers, blood and cerebrospinal fluid biomarkers, and advanced genetic testing, a more accurate and earlier recognition of MSA should be possible, even in the prodromal stages. This has important implications as misdiagnosis can result in inappropriate treatment, patient and family distress, and erroneous eligibility for clinical trials of disease-modifying drugs. Copyright © 2017 Elsevier B.V. All rights reserved.

[Central nervous system involvement in systemic lupus erythematosus - diagnosis and therapy].

Science.gov (United States)

Szmyrka, Magdalena

Nervous system involvement in lupus belongs to its severe complications and significantly impacts its prognosis. Neuropsychiatric lupus includes 19 disease manifestations concerning both central and peripheral nervous system. This paper presents clinical aspects of central nervous system involvement in lupus. It reviews its epidemiology, risk factors and principles of diagnosis and therapy.

A fault diagnosis method based on signed directed graph and matrix for nuclear power plants

International Nuclear Information System (INIS)

Liu, Yong-Kuo; Wu, Guo-Hua; Xie, Chun-Li; Duan, Zhi-Yong; Peng, Min-Jun; Li, Meng-Kun

2016-01-01

Highlights: • “Rules matrix” is proposed for FDD. • “State matrix” is proposed to solve SDG online inference. • SDG inference and search method are combined for FDD. - Abstract: In order to solve SDG online fault diagnosis and inference, matrix diagnosis and inference methods are proposed for fault detection and diagnosis (FDD). Firstly, “rules matrix” based on SDG model is used for FDD. Secondly, “status matrix” is proposed to achieve SDG online inference. According to different diagnosis results, “status matrix” is applied for the depth-first search and the breadth-first search respectively to find the propagation paths of each fault. Finally, the SDG model of the secondary-loop system in pressurized water reactor (PWR) is built to verify the effectiveness of the proposed method. The simulation experiment results indicate that the “status matrix” used for online inference can be used to find the fault propagation paths and to explain the causes for fault. Therefore, it can be concluded that the proposed method is one of the fault diagnosis for nuclear power plants (NPPs), which can be used to facilitate the development of fault diagnostic system.

A fault diagnosis method based on signed directed graph and matrix for nuclear power plants

Energy Technology Data Exchange (ETDEWEB)

Liu, Yong-Kuo, E-mail: LYK08@126.com [Fundamental Science on Nuclear Safety and Simulation Technology Laboratory, Harbin Engineering University, Harbin 150001 (China); Wu, Guo-Hua [Fundamental Science on Nuclear Safety and Simulation Technology Laboratory, Harbin Engineering University, Harbin 150001 (China); Institute of Nuclear Energy Technology, Tsinghua University, Beijing 100084 (China); Xie, Chun-Li [Traffic College, Northeast Forestry University, Harbin, 150040 (China); Duan, Zhi-Yong; Peng, Min-Jun; Li, Meng-Kun [Fundamental Science on Nuclear Safety and Simulation Technology Laboratory, Harbin Engineering University, Harbin 150001 (China)

2016-02-15

Highlights: • “Rules matrix” is proposed for FDD. • “State matrix” is proposed to solve SDG online inference. • SDG inference and search method are combined for FDD. - Abstract: In order to solve SDG online fault diagnosis and inference, matrix diagnosis and inference methods are proposed for fault detection and diagnosis (FDD). Firstly, “rules matrix” based on SDG model is used for FDD. Secondly, “status matrix” is proposed to achieve SDG online inference. According to different diagnosis results, “status matrix” is applied for the depth-first search and the breadth-first search respectively to find the propagation paths of each fault. Finally, the SDG model of the secondary-loop system in pressurized water reactor (PWR) is built to verify the effectiveness of the proposed method. The simulation experiment results indicate that the “status matrix” used for online inference can be used to find the fault propagation paths and to explain the causes for fault. Therefore, it can be concluded that the proposed method is one of the fault diagnosis for nuclear power plants (NPPs), which can be used to facilitate the development of fault diagnostic system.

Satellite Fault Diagnosis Using Support Vector Machines Based on a Hybrid Voting Mechanism

Science.gov (United States)

Yang, Shuqiang; Zhu, Xiaoqian; Jin, Songchang; Wang, Xiang

2014-01-01

The satellite fault diagnosis has an important role in enhancing the safety, reliability, and availability of the satellite system. However, the problem of enormous parameters and multiple faults makes a challenge to the satellite fault diagnosis. The interactions between parameters and misclassifications from multiple faults will increase the false alarm rate and the false negative rate. On the other hand, for each satellite fault, there is not enough fault data for training. To most of the classification algorithms, it will degrade the performance of model. In this paper, we proposed an improving SVM based on a hybrid voting mechanism (HVM-SVM) to deal with the problem of enormous parameters, multiple faults, and small samples. Many experimental results show that the accuracy of fault diagnosis using HVM-SVM is improved. PMID:25215324

Satellite Fault Diagnosis Using Support Vector Machines Based on a Hybrid Voting Mechanism

Directory of Open Access Journals (Sweden)

Hong Yin

2014-01-01

Full Text Available The satellite fault diagnosis has an important role in enhancing the safety, reliability, and availability of the satellite system. However, the problem of enormous parameters and multiple faults makes a challenge to the satellite fault diagnosis. The interactions between parameters and misclassifications from multiple faults will increase the false alarm rate and the false negative rate. On the other hand, for each satellite fault, there is not enough fault data for training. To most of the classification algorithms, it will degrade the performance of model. In this paper, we proposed an improving SVM based on a hybrid voting mechanism (HVM-SVM to deal with the problem of enormous parameters, multiple faults, and small samples. Many experimental results show that the accuracy of fault diagnosis using HVM-SVM is improved.

Automated Diagnosis and Control of Complex Systems

Science.gov (United States)

Kurien, James; Plaunt, Christian; Cannon, Howard; Shirley, Mark; Taylor, Will; Nayak, P.; Hudson, Benoit; Bachmann, Andrew; Brownston, Lee; Hayden, Sandra;

Analysis of operators' diagnosis tasks based on cognitive process

International Nuclear Information System (INIS)

Zhou Yong; Zhang Li

2012-01-01

Diagnosis tasks in nuclear power plants characterized as high-dynamic uncertainties are complex reasoning tasks. Diagnosis errors are the main causes for the error of commission. Firstly, based on mental model theory and perception/action cycle theory, a cognitive model for analyzing operators' diagnosis tasks is proposed. Then, the model is used to investigate a trip event which occurred at crystal river nuclear power plant. The application demonstrates typical cognitive bias and mistakes which operators may make when performing diagnosis tasks. They mainly include the strong confirmation tendency, difficulty to produce complete hypothesis sets, group mindset, non-systematic errors in hypothesis testing, and etc. (authors)

A diagnosis system for plant operation support

International Nuclear Information System (INIS)

Sundheimer, S.; Lorenzetti, J.; Lamana, C.

1990-01-01

The present article describes a diagnosis system for abnormal power plant events. The design is modular and uses a shell written in C languaje and a knowledge basis that can be changed easily. At present the system works with a reduced knowledge for primary and secondery leacks. The mitigation procedure is being written with the help of operation staff

Fault diagnosis of main coolant pump in the nuclear power station based on the principal component analysis

International Nuclear Information System (INIS)

Feng Junting; Xu Mi; Wang Guizeng

2003-01-01

The fault diagnosis method based on principal component analysis is studied. The fault character direction storeroom of fifteen parameters abnormity is built in the simulation for the main coolant pump of nuclear power station. The measuring data are analyzed, and the results show that it is feasible for the fault diagnosis system of main coolant pump in the nuclear power station

Study of expert system of fault diagnosis for nuclear power plant

International Nuclear Information System (INIS)

Chen Zhihui; Xia Hong; Liu Miao

2005-01-01

Based on the fault features of Nuclear Power Plant, the ES (expert system) of fault diagnosis has been programmed. The knowledge in the ES adopts the production systems, which can express the certain and uncertain knowledge. For certain knowledge, the simple reasoning mechanism of prepositional logic is adopted. For the uncertain knowledge, CF (certain factor) is used to express the uncertain, thus to set up the reasoning mechanism. In order to solve the 'bottleneck' problem for knowledge acquisition, rough set theory is incorporated into the fault diagnose system and the reduction algorithm based on the discernibility matrix is improved. In the improved algorithm, the measure of attribute importance first calculate the attribute which have the same value in the same decision-sort, then calculate the degrees of attribute in the discernibility matrix. Several different faults have been diagnosed on some emulator with this expert system. (authors)

Sensor fault diagnosis of aero-engine based on divided flight status

Science.gov (United States)

Zhao, Zhen; Zhang, Jun; Sun, Yigang; Liu, Zhexu

2017-11-01

Fault diagnosis and safety analysis of an aero-engine have attracted more and more attention in modern society, whose safety directly affects the flight safety of an aircraft. In this paper, the problem concerning sensor fault diagnosis is investigated for an aero-engine during the whole flight process. Considering that the aero-engine is always working in different status through the whole flight process, a flight status division-based sensor fault diagnosis method is presented to improve fault diagnosis precision for the aero-engine. First, aero-engine status is partitioned according to normal sensor data during the whole flight process through the clustering algorithm. Based on that, a diagnosis model is built for each status using the principal component analysis algorithm. Finally, the sensors are monitored using the built diagnosis models by identifying the aero-engine status. The simulation result illustrates the effectiveness of the proposed method.

Sensor fault diagnosis of aero-engine based on divided flight status.

Science.gov (United States)

Zhao, Zhen; Zhang, Jun; Sun, Yigang; Liu, Zhexu

2017-11-01

Fault diagnosis and safety analysis of an aero-engine have attracted more and more attention in modern society, whose safety directly affects the flight safety of an aircraft. In this paper, the problem concerning sensor fault diagnosis is investigated for an aero-engine during the whole flight process. Considering that the aero-engine is always working in different status through the whole flight process, a flight status division-based sensor fault diagnosis method is presented to improve fault diagnosis precision for the aero-engine. First, aero-engine status is partitioned according to normal sensor data during the whole flight process through the clustering algorithm. Based on that, a diagnosis model is built for each status using the principal component analysis algorithm. Finally, the sensors are monitored using the built diagnosis models by identifying the aero-engine status. The simulation result illustrates the effectiveness of the proposed method.

Diagnosis and Early Warning of Wind Turbine Faults Based on Cluster Analysis Theory and Modified ANFIS

Directory of Open Access Journals (Sweden)

Quan Zhou

2017-07-01

Full Text Available The construction of large-scale wind farms results in a dramatic increase of wind turbine (WT faults. The failure mode is also becoming increasingly complex. This study proposes a new model for early warning and diagnosis of WT faults to solve the problem of Supervisory Control And Data Acquisition (SCADA systems, given that the traditional threshold method cannot provide timely warning. First, the characteristic quantity of fault early warning and diagnosis analyzed by clustering analysis can obtain in advance abnormal data in the normal threshold range by considering the effects of wind speed. Based on domain knowledge, Adaptive Neuro-fuzzy Inference System (ANFIS is then modified to establish the fault early warning and diagnosis model. This approach improves the accuracy of the model under the condition of absent and sparse training data. Case analysis shows that the effect of the early warning and diagnosis model in this study is better than that of the traditional threshold method.

Computer-aided detection systems to improve lung cancer early diagnosis: state-of-the-art and challenges

International Nuclear Information System (INIS)

Traverso, A; Lopez Torres, E; Cerello, P; Fantacci, M E

2017-01-01

Lung cancer is one of the most lethal types of cancer, because its early diagnosis is not good enough. In fact, the detection of pulmonary nodule, potential lung cancers, in Computed Tomography scans is a very challenging and time-consuming task for radiologists. To support radiologists, researchers have developed Computer-Aided Diagnosis (CAD) systems for the automated detection of pulmonary nodules in chest Computed Tomography scans. Despite the high level of technological developments and the proved benefits on the overall detection performance, the usage of Computer-Aided Diagnosis in clinical practice is far from being a common procedure. In this paper we investigate the causes underlying this discrepancy and present a solution to tackle it: the M5L WEB- and Cloud-based on-demand Computer-Aided Diagnosis. In addition, we prove how the combination of traditional imaging processing techniques with state-of-art advanced classification algorithms allows to build a system whose performance could be much larger than any Computer-Aided Diagnosis developed so far. This outcome opens the possibility to use the CAD as clinical decision support for radiologists. (paper)

Computer-aided detection systems to improve lung cancer early diagnosis: state-of-the-art and challenges

Science.gov (United States)

Traverso, A.; Lopez Torres, E.; Fantacci, M. E.; Cerello, P.

2017-05-01

Lung cancer is one of the most lethal types of cancer, because its early diagnosis is not good enough. In fact, the detection of pulmonary nodule, potential lung cancers, in Computed Tomography scans is a very challenging and time-consuming task for radiologists. To support radiologists, researchers have developed Computer-Aided Diagnosis (CAD) systems for the automated detection of pulmonary nodules in chest Computed Tomography scans. Despite the high level of technological developments and the proved benefits on the overall detection performance, the usage of Computer-Aided Diagnosis in clinical practice is far from being a common procedure. In this paper we investigate the causes underlying this discrepancy and present a solution to tackle it: the M5L WEB- and Cloud-based on-demand Computer-Aided Diagnosis. In addition, we prove how the combination of traditional imaging processing techniques with state-of-art advanced classification algorithms allows to build a system whose performance could be much larger than any Computer-Aided Diagnosis developed so far. This outcome opens the possibility to use the CAD as clinical decision support for radiologists.

Development of a variable structure-based fault detection and diagnosis strategy applied to an electromechanical system

Science.gov (United States)

Gadsden, S. Andrew; Kirubarajan, T.

2017-05-01

Signal processing techniques are prevalent in a wide range of fields: control, target tracking, telecommunications, robotics, fault detection and diagnosis, and even stock market analysis, to name a few. Although first introduced in the 1950s, the most popular method used for signal processing and state estimation remains the Kalman filter (KF). The KF offers an optimal solution to the estimation problem under strict assumptions. Since this time, a number of other estimation strategies and filters were introduced to overcome robustness issues, such as the smooth variable structure filter (SVSF). In this paper, properties of the SVSF are explored in an effort to detect and diagnosis faults in an electromechanical system. The results are compared with the KF method, and future work is discussed.

Nuclear Power Plants Fault Diagnosis Method Based on Data Fusion

International Nuclear Information System (INIS)

Xie Chunli; Liu Yongkuo; Xia Hong

2009-01-01

The data fusion is a method suit for complex system fault diagnosis such as nuclear power plants, which is multisource information processing technology. This paper uses data fusion information hierarchical thinking and divides nuclear power plants fault diagnosis into three levels. Data level adopts data mining method to handle data and reduction attributes. Feature level uses three parallel neural networks to deal with attributes of data level reduction and the outputs of three networks are as the basic probability assignment of Dempster-Shafer (D-S) evidence theory. The improved D-S evidence theory synthesizes the outputs of neural networks in decision level, which conquer the traditional D-S evidence theory limitation which can't dispose conflict information. The diagnosis method was tested using correlation data of literature. The test results indicate that the data fusion diagnosis system can diagnose nuclear power plants faults accurately and the method has application value. (authors)

Fault diagnosis for agitator driving system in a high temperature reduction reactor

Energy Technology Data Exchange (ETDEWEB)

Park, Gee Young; Hong, Dong Hee; Jung, Jae Hoo; Kim, Young Hwan; Jin, Jae Hyun; Yoon, Ji Sup [KAERI, Taejon (Korea, Republic of)

2003-10-01

In this paper, a preliminary study for development of a fault diagnosis is presented for monitoring and diagnosing faults in the agitator driving system of a high temperature reduction reactor. In order to identify a fault occurrence and classify the fault cause, vibration signals measured by accelerometers on the outer shroud of the agitator driving system are firstly decomposed by Wavelet Transform (WT) and the features corresponding to each fault type are extracted. For the diagnosis, the fuzzy ARTMAP is employed and thereby, based on the features extracted from the WT, the robust fault classifier can be implemented with a very short training time - a single training epoch and a single learning iteration is sufficient for training the fault classifier. The test results demonstrate satisfactory classification for the faults pre-categorized from considerations of possible occurrence during experiments on a small-scale reduction reactor.

A Statistical Parameter Analysis and SVM Based Fault Diagnosis Strategy for Dynamically Tuned Gyroscopes

Institute of Scientific and Technical Information of China (English)

无

2007-01-01

Gyro's fault diagnosis plays a critical role in inertia navigation systems for higher reliability and precision. A new fault diagnosis strategy based on the statistical parameter analysis (SPA) and support vector machine(SVM) classification model was proposed for dynamically tuned gyroscopes (DTG). The SPA, a kind of time domain analysis approach, was introduced to compute a set of statistical parameters of vibration signal as the state features of DTG, with which the SVM model, a novel learning machine based on statistical learning theory (SLT), was applied and constructed to train and identify the working state of DTG. The experimental results verify that the proposed diagnostic strategy can simply and effectively extract the state features of DTG, and it outperforms the radial-basis function (RBF) neural network based diagnostic method and can more reliably and accurately diagnose the working state of DTG.

Infrared thermography based on artificial intelligence as a screening method for carpal tunnel syndrome diagnosis.

Science.gov (United States)

Jesensek Papez, B; Palfy, M; Mertik, M; Turk, Z

2009-01-01

This study further evaluated a computer-based infrared thermography (IRT) system, which employs artificial neural networks for the diagnosis of carpal tunnel syndrome (CTS) using a large database of 502 thermal images of the dorsal and palmar side of 132 healthy and 119 pathological hands. It confirmed the hypothesis that the dorsal side of the hand is of greater importance than the palmar side when diagnosing CTS thermographically. Using this method it was possible correctly to classify 72.2% of all hands (healthy and pathological) based on dorsal images and > 80% of hands when only severely affected and healthy hands were considered. Compared with the gold standard electromyographic diagnosis of CTS, IRT cannot be recommended as an adequate diagnostic tool when exact severity level diagnosis is required, however we conclude that IRT could be used as a screening tool for severe cases in populations with high ergonomic risk factors of CTS.

Computer-Aided Characterization and Diagnosis of Diffuse Liver Diseases Based on Ultrasound Imaging: A Review.

Science.gov (United States)

Bharti, Puja; Mittal, Deepti; Ananthasivan, Rupa

2016-04-19

Diffuse liver diseases, such as hepatitis, fatty liver, and cirrhosis, are becoming a leading cause of fatality and disability all over the world. Early detection and diagnosis of these diseases is extremely important to save lives and improve effectiveness of treatment. Ultrasound imaging, a noninvasive diagnostic technique, is the most commonly used modality for examining liver abnormalities. However, the accuracy of ultrasound-based diagnosis depends highly on expertise of radiologists. Computer-aided diagnosis systems based on ultrasound imaging assist in fast diagnosis, provide a reliable "second opinion" for experts, and act as an effective tool to measure response of treatment on patients undergoing clinical trials. In this review, we first describe appearance of liver abnormalities in ultrasound images and state the practical issues encountered in characterization of diffuse liver diseases that can be addressed by software algorithms. We then discuss computer-aided diagnosis in general with features and classifiers relevant to diffuse liver diseases. In later sections of this paper, we review the published studies and describe the key findings of those studies. A concise tabular summary comparing image database, features extraction, feature selection, and classification algorithms presented in the published studies is also exhibited. Finally, we conclude with a summary of key findings and directions for further improvements in the areas of accuracy and objectiveness of computer-aided diagnosis. © The Author(s) 2016.

Qualitative Event-Based Diagnosis: Case Study on the Second International Diagnostic Competition

Science.gov (United States)

Daigle, Matthew; Roychoudhury, Indranil

2010-01-01

We describe a diagnosis algorithm entered into the Second International Diagnostic Competition. We focus on the first diagnostic problem of the industrial track of the competition in which a diagnosis algorithm must detect, isolate, and identify faults in an electrical power distribution testbed and provide corresponding recovery recommendations. The diagnosis algorithm embodies a model-based approach, centered around qualitative event-based fault isolation. Faults produce deviations in measured values from model-predicted values. The sequence of these deviations is matched to those predicted by the model in order to isolate faults. We augment this approach with model-based fault identification, which determines fault parameters and helps to further isolate faults. We describe the diagnosis approach, provide diagnosis results from running the algorithm on provided example scenarios, and discuss the issues faced, and lessons learned, from implementing the approach

[Definition of the Diagnosis Osteomyelitis-Osteomyelitis Diagnosis Score (ODS)].

Science.gov (United States)

Schmidt, H G K; Tiemann, A H; Braunschweig, R; Diefenbeck, M; Bühler, M; Abitzsch, D; Haustedt, N; Walter, G; Schoop, R; Heppert, V; Hofmann, G O; Glombitza, M; Grimme, C; Gerlach, U-J; Flesch, I

2011-08-01

The disease "osteomyelitis" is characterised by different symptoms and parameters. Decisive roles in the development of the disease are played by the causative bacteria, the route of infection and the individual defense mechanisms of the host. The diagnosis is based on different symptoms and findings from the clinical history, clinical symptoms, laboratory results, diagnostic imaging, microbiological and histopathological analyses. While different osteomyelitis classifications have been published, there is to the best of our knowledge no score that gives information how sure the diagnosis "osteomyelitis" is in general. For any scientific study of a disease a valid definition is essential. We have developed a special osteomyelitis diagnosis score for the reliable classification of clinical, laboratory and technical findings. The score is based on five diagnostic procedures: 1) clinical history and risk factors, 2) clinical examination and laboratory results, 3) diagnostic imaging (ultrasound, radiology, CT, MRI, nuclear medicine and hybrid methods), 4) microbiology, and 5) histopathology. Each diagnostic procedure is related to many individual findings, which are weighted by a score system, in order to achieve a relevant value for each assessment. If the sum of the five diagnostic criteria is 18 or more points, the diagnosis of osteomyelitis can be viewed as "safe" (diagnosis class A). Between 8-17 points the diagnosis is "probable" (diagnosis class B). Less than 8 points means that the diagnosis is "possible, but unlikely" (class C diagnosis). Since each parameter can score six points at a maximum, a reliable diagnosis can only be achieved if at least 3 parameters are scored with 6 points. The osteomyelitis diagnosis score should help to avoid the false description of a clinical presentation as "osteomyelitis". A safe diagnosis is essential for the aetiology, treatment and outcome studies of osteomyelitis. © Georg Thieme Verlag KG Stuttgart · New York.

Method of modelization assistance with bond graphs and application to qualitative diagnosis of physical systems; Methode d'aide a la modelisation par graphes de liaison et utilisation pour le diagnostic qualitatif de systemes physiques

Energy Technology Data Exchange (ETDEWEB)

Lucas, B.

1994-05-15

After having recalled the usual diagnosis techniques (failure index, decision tree) and those based on an artificial intelligence approach, the author reports a research aimed at exploring the knowledge and model generation technique. He focuses on the design of an aid to model generation tool and aid-to-diagnosis tool. The bond graph technique is shown to be adapted to the aid to model generation, and is then adapted to the aid to diagnosis. The developed tool is applied to three projects: DIADEME (a diagnosis system based on physical model), the improvement of the SEXTANT diagnosis system (an expert system for transient analysis), and the investigation on an Ariane 5 launcher component. Notably, the author uses the Reiter and Greiner algorithm

Data-driven technology for engineering systems health management design approach, feature construction, fault diagnosis, prognosis, fusion and decisions

CERN Document Server

Niu, Gang

2017-01-01

This book introduces condition-based maintenance (CBM)/data-driven prognostics and health management (PHM) in detail, first explaining the PHM design approach from a systems engineering perspective, then summarizing and elaborating on the data-driven methodology for feature construction, as well as feature-based fault diagnosis and prognosis. The book includes a wealth of illustrations and tables to help explain the algorithms, as well as practical examples showing how to use this tool to solve situations for which analytic solutions are poorly suited. It equips readers to apply the concepts discussed in order to analyze and solve a variety of problems in PHM system design, feature construction, fault diagnosis and prognosis.

Improvement of Roller Bearing Diagnosis with Unlabeled Data Using Cut Edge Weight Confidence Based Tritraining

Directory of Open Access Journals (Sweden)

Wei-Li Qin

2016-01-01

Full Text Available Roller bearings are one of the most commonly used components in rotational machines. The fault diagnosis of roller bearings thus plays an important role in ensuring the safe functioning of the mechanical systems. However, in most cases of bearing fault diagnosis, there are limited number of labeled data to achieve a proper fault diagnosis. Therefore, exploiting unlabeled data plus few labeled data, this paper proposed a roller bearing fault diagnosis method based on tritraining to improve roller bearing diagnosis performance. To overcome the noise brought by wrong labeling into the classifiers training process, the cut edge weight confidence is introduced into the diagnosis framework. Besides a small trick called suspect principle is adopted to avoid overfitting problem. The proposed method is validated in two independent roller bearing fault experiment vibrational signals that both include three types of faults: inner-ring fault, outer-ring fault, and rolling element fault. The results demonstrate the desirable diagnostic performance improvement by the proposed method in the extreme situation where there is only limited number of labeled data.

Acoustic Emission Monitoring of Incipient Failure in Journal Bearings( III ) - Development of AE Diagnosis System for Journal Bearings -

International Nuclear Information System (INIS)

Chung, Min Hwa; Cho, Yong Sang; Yoon, Dong Jin; Kwon, Oh Yang

1996-01-01

For the condition monitoring of the journal bearing in rotating machinery, a system for their diagnosis by acoustic emission(AE) was developed. AE has been used to detect abnormal conditions in the bearing system. It was found from the field application study as well as the laboratory experiment using a simulated journal bearing system that AE RMS voltage was the most efficient parameter for the purpose of current study. Based on the above results, algorithms and judgement criteria for the diagnosis system was established. The system is composed of four parts as follows: the sensing part including AE sensor and preamplifier, the signal processing part for RMS-to-DC conversion to measure AE ms voltage, the interface part for transferring RMS voltage data into PC using A/D converter, and the software part including the graphic display of bearing conditions and the diagnosis program

An intelligent approach for cooling radiator fault diagnosis based on infrared thermal image processing technique

International Nuclear Information System (INIS)

Taheri-Garavand, Amin; Ahmadi, Hojjat; Omid, Mahmoud; Mohtasebi, Seyed Saeid; Mollazade, Kaveh; Russell Smith, Alan John; Carlomagno, Giovanni Maria

2015-01-01

This research presents a new intelligent fault diagnosis and condition monitoring system for classification of different conditions of cooling radiator using infrared thermal images. The system was adopted to classify six types of cooling radiator faults; radiator tubes blockage, radiator fins blockage, loose connection between fins and tubes, radiator door failure, coolant leakage, and normal conditions. The proposed system consists of several distinct procedures including thermal image acquisition, image pre-processing, image processing, two-dimensional discrete wavelet transform (2D-DWT), feature extraction, feature selection using a genetic algorithm (GA), and finally classification by artificial neural networks (ANNs). The 2D-DWT is implemented to decompose the thermal images. Subsequently, statistical texture features are extracted from the original images and are decomposed into thermal images. The significant selected features are used to enhance the performance of the designed ANN classifier for the 6 types of cooling radiator conditions (output layer) in the next stage. For the tested system, the input layer consisted of 16 neurons based on the feature selection operation. The best performance of ANN was obtained with a 16-6-6 topology. The classification results demonstrated that this system can be employed satisfactorily as an intelligent condition monitoring and fault diagnosis for a class of cooling radiator. - Highlights: • Intelligent fault diagnosis of cooling radiator using thermal image processing. • Thermal image processing in a multiscale representation structure by 2D-DWT. • Selection features based on a hybrid system that uses both GA and ANN. • Application of ANN as classifier. • Classification accuracy of fault detection up to 93.83%

A Fault Diagnosis Methodology for Gear Pump Based on EEMD and Bayesian Network.

Science.gov (United States)

Liu, Zengkai; Liu, Yonghong; Shan, Hongkai; Cai, Baoping; Huang, Qing

2015-01-01

This paper proposes a fault diagnosis methodology for a gear pump based on the ensemble empirical mode decomposition (EEMD) method and the Bayesian network. Essentially, the presented scheme is a multi-source information fusion based methodology. Compared with the conventional fault diagnosis with only EEMD, the proposed method is able to take advantage of all useful information besides sensor signals. The presented diagnostic Bayesian network consists of a fault layer, a fault feature layer and a multi-source information layer. Vibration signals from sensor measurement are decomposed by the EEMD method and the energy of intrinsic mode functions (IMFs) are calculated as fault features. These features are added into the fault feature layer in the Bayesian network. The other sources of useful information are added to the information layer. The generalized three-layer Bayesian network can be developed by fully incorporating faults and fault symptoms as well as other useful information such as naked eye inspection and maintenance records. Therefore, diagnostic accuracy and capacity can be improved. The proposed methodology is applied to the fault diagnosis of a gear pump and the structure and parameters of the Bayesian network is established. Compared with artificial neural network and support vector machine classification algorithms, the proposed model has the best diagnostic performance when sensor data is used only. A case study has demonstrated that some information from human observation or system repair records is very helpful to the fault diagnosis. It is effective and efficient in diagnosing faults based on uncertain, incomplete information.

Development of computer-aided diagnosis systems in radiology

International Nuclear Information System (INIS)

Higashida, Yoshiharu; Arimura, Hidetaka; Kumazawa, Seiji; Morishita, Junji; Sakai, Shuji

2006-01-01

Computer-aided diagnosis (CAD) is a practice done by medical doctors based on computer image analysis as the second opinion, and CAD studies have been government-adopted projects. CAD is already on popular practice in the cancers of the breast by mammography, lung by flat plate and CT images, and large bowel by CT colonoscopy. This paper describes four examples of authors' actual CAD investigations. First, the temporal subtraction image analysis by CAD is for the detection of abnormality in the chest by radiographs taken at different times. Examples are shown in cases of interstitial pneumonia and lung cancer out of 34 patients with diffuse lung diseases. Second, development of CAD system is recorded for detection of aneurysm by the brain MR angiography (MRA). Third is the CAD detection of fascicles in cerebral white matters by the diffuse tensor MRI, which will help the surgery for brain tumors. Final is an automated patient recognition based on an image-matching technique using previous chest radiographs in the picture archiving and communication systems. This is on the radiograph giving biological fingerprints of the patients. CAD will be applied in a wider field of medicare not only in imaging technology. (T.I)

The Use of LMS AMESim in the Fault Diagnosis of a Commercial PEM Fuel Cell System

Directory of Open Access Journals (Sweden)

Reem Izzeldin Salim

2018-01-01

Full Text Available The world’s pollution rates have been increasing exponentially due to the many reckless lifestyle practices of human beings as well as their choices of contaminating power sources. Eventually, this lead to a worldwide awareness on the risks of those power sources, and in turn, a movement towards the exploration and deployment of several green technologies emerged. Proton Exchange Membrane Fuel cells (PEMFCs are one of those green technologies. However, in order to be able to successfully and efficiently deploy PEMFC systems, a solid fault diagnosis scheme is needed. The development of accurate model based fault diagnosis schemes has been imposing a lot of challenge and difficulty on researchers due to the high complexity of the PEMFC system. Furthermore, confidentiality issues with the manufacturer can also impose further constraints on the model development of a commercial PEMFC system. In this work, an approach to develop an accurate PEMFC system model despite the lack of crucial system information is presented through the use of Siemens LMS AMESim software. The developed model is then used to develop a fault diagnosis scheme that is able to detect and isolate five system faults.

Active fault diagnosis based on stochastic tests

DEFF Research Database (Denmark)

Poulsen, Niels Kjølstad; Niemann, Hans Henrik

2008-01-01

The focus of this paper is on stochastic change detection applied in connection with active fault diagnosis (AFD). An auxiliary input signal is applied in AFD. This signal injection in the system will in general allow us to obtain a fast change detection/isolation by considering the output...

Numerical model for thermoeconomic diagnosis in commercial transcritical/subcritical booster refrigeration systems

DEFF Research Database (Denmark)

Ommen, Torben; Elmegaard, Brian

2012-01-01

cycle supplying refrigerant for evaporators in both chilled and frozen display cases. In the paper, thermoeconomic theory is used to establish the cost of cooling at each individual temperature level based on operating costs.With a high amount of operating systems, faulty operation becomes an economic......, and environmental, interest. A general solution for evaluation of these systems is considered, with the objective to reduce cost and power consumption of malfunctioning equipment in operation. An analysis of the use of thermoeconomic diagnosis methods is required, as these methods may prove applicable...

Transient pattern analysis for fault detection and diagnosis of HVAC systems

International Nuclear Information System (INIS)

Cho, Sung-Hwan; Yang, Hoon-Cheol; Zaheer-uddin, M.; Ahn, Byung-Cheon

2005-01-01

Modern building HVAC systems are complex and consist of a large number of interconnected sub-systems and components. In the event of a fault, it becomes very difficult for the operator to locate and isolate the faulty component in such large systems using conventional fault detection methods. In this study, transient pattern analysis is explored as a tool for fault detection and diagnosis of an HVAC system. Several tests involving different fault replications were conducted in an environmental chamber test facility. The results show that the evolution of fault residuals forms clear and distinct patterns that can be used to isolate faults. It was found that the time needed to reach steady state for a typical building HVAC system is at least 50-60 min. This means incorrect diagnosis of faults can happen during online monitoring if the transient pattern responses are not considered in the fault detection and diagnosis analysis

A Novel Method for Inverter Faults Detection and Diagnosis in PMSM Drives of HEVs based on Discrete Wavelet Transform

Directory of Open Access Journals (Sweden)

AKTAS, M.

2012-11-01

Full Text Available The paper proposes a novel method, based on wavelet decomposition, for detection and diagnosis of faults (switch short-circuits and switch open-circuits in the driving systems with Field Oriented Controlled Permanent Magnet Synchro?nous Motors (PMSM of Hybrid Electric Vehicles. The fault behaviour of the analyzed system was simulated by Matlab/SIMULINK R2010a. The stator currents during transients were analysed up to the sixth level detail wavelet decomposition by Symlet2 wavelet. The results prove that the proposed fault diagnosis system have very good capabilities.

Model-Based Diagnosis and Prognosis of a Water Recycling System

Data.gov (United States)

National Aeronautics and Space Administration — A water recycling system (WRS) deployed at NASA Ames Research Center’s Sustainability Base (an energy efficient office building that integrates some novel...

Cooperative research and development for artificial intelligence based reactor diagnostic system

International Nuclear Information System (INIS)

Reifman, J.; Wei, T.Y.C.; Abboud, R.G.; Chasensky, T.M.

1994-01-01

Artificial Intelligence (AI) techniques in the form of knowledge-based Expert Systems (ESs) have been proposed to provide on-line decision-making support for plant operators during both normal and emergency conditions. However, in spite of the great interest in these advanced techniques, their application in the diagnosis of large-scale processes has not yet reached its full potential because of limitations of the knowledge base. These limitations include problems with knowledge acquisition and the use of an event-oriented approach for process diagnosis. The knowledge base of process diagnosis ESs is generally acquired in a heuristic fashion through empirical associations between plant symptoms and component malfunctions with no reliance on fundamental physical principles. This nonsystematic construction of the knowledge base causes, among other problems, the encoded information to be biased and limited towards the developer's own experience and judgmental knowledge. The use of an event-oriented approach for process diagnosis requires the developer of the knowledge base to anticipate and formulate rules to cover every conceivable plant situation. In addition to yielding a large knowledge base, an undesirable characteristic for an on-line real-time advisory system, an event-oriented approach for diagnosis of large and complex thermal-hydraulic (T-H) based processes cannot guarantee functional completeness and is likely to fail under unanticipated circumstances. Hence, these limitations preclude an effective verification and validation of the knowledge base which is required in industrial applications. In contrast to the heuristic construction of a rigid knowledge base that uses an event-oriented approach for process diagnosis, the authors propose a different approach that involves the systematic construction of a hierarchical knowledge base with two levels

Multi-Stage Feature Selection by Using Genetic Algorithms for Fault Diagnosis in Gearboxes Based on Vibration Signal

Directory of Open Access Journals (Sweden)

Mariela Cerrada

2015-09-01

Full Text Available There are growing demands for condition-based monitoring of gearboxes, and techniques to improve the reliability, effectiveness and accuracy for fault diagnosis are considered valuable contributions. Feature selection is still an important aspect in machine learning-based diagnosis in order to reach good performance in the diagnosis system. The main aim of this research is to propose a multi-stage feature selection mechanism for selecting the best set of condition parameters on the time, frequency and time-frequency domains, which are extracted from vibration signals for fault diagnosis purposes in gearboxes. The selection is based on genetic algorithms, proposing in each stage a new subset of the best features regarding the classifier performance in a supervised environment. The selected features are augmented at each stage and used as input for a neural network classifier in the next step, while a new subset of feature candidates is treated by the selection process. As a result, the inherent exploration and exploitation of the genetic algorithms for finding the best solutions of the selection problem are locally focused. The Sensors 2015, 15 23904 approach is tested on a dataset from a real test bed with several fault classes under different running conditions of load and velocity. The model performance for diagnosis is over 98%.

A modular neural network scheme applied to fault diagnosis in electric power systems.

Science.gov (United States)

Flores, Agustín; Quiles, Eduardo; García, Emilio; Morant, Francisco; Correcher, Antonio

2014-01-01

This work proposes a new method for fault diagnosis in electric power systems based on neural modules. With this method the diagnosis is performed by assigning a neural module for each type of component comprising the electric power system, whether it is a transmission line, bus or transformer. The neural modules for buses and transformers comprise two diagnostic levels which take into consideration the logic states of switches and relays, both internal and back-up, with the exception of the neural module for transmission lines which also has a third diagnostic level which takes into account the oscillograms of fault voltages and currents as well as the frequency spectrums of these oscillograms, in order to verify if the transmission line had in fact been subjected to a fault. One important advantage of the diagnostic system proposed is that its implementation does not require the use of a network configurator for the system; it does not depend on the size of the power network nor does it require retraining of the neural modules if the power network increases in size, making its application possible to only one component, a specific area, or the whole context of the power system.

A Bayesian least squares support vector machines based framework for fault diagnosis and failure prognosis

Science.gov (United States)

Khawaja, Taimoor Saleem

A high-belief low-overhead Prognostics and Health Management (PHM) system is desired for online real-time monitoring of complex non-linear systems operating in a complex (possibly non-Gaussian) noise environment. This thesis presents a Bayesian Least Squares Support Vector Machine (LS-SVM) based framework for fault diagnosis and failure prognosis in nonlinear non-Gaussian systems. The methodology assumes the availability of real-time process measurements, definition of a set of fault indicators and the existence of empirical knowledge (or historical data) to characterize both nominal and abnormal operating conditions. An efficient yet powerful Least Squares Support Vector Machine (LS-SVM) algorithm, set within a Bayesian Inference framework, not only allows for the development of real-time algorithms for diagnosis and prognosis but also provides a solid theoretical framework to address key concepts related to classification for diagnosis and regression modeling for prognosis. SVM machines are founded on the principle of Structural Risk Minimization (SRM) which tends to find a good trade-off between low empirical risk and small capacity. The key features in SVM are the use of non-linear kernels, the absence of local minima, the sparseness of the solution and the capacity control obtained by optimizing the margin. The Bayesian Inference framework linked with LS-SVMs allows a probabilistic interpretation of the results for diagnosis and prognosis. Additional levels of inference provide the much coveted features of adaptability and tunability of the modeling parameters. The two main modules considered in this research are fault diagnosis and failure prognosis. With the goal of designing an efficient and reliable fault diagnosis scheme, a novel Anomaly Detector is suggested based on the LS-SVM machines. The proposed scheme uses only baseline data to construct a 1-class LS-SVM machine which, when presented with online data is able to distinguish between normal behavior

DNA methylation-based classification of central nervous system tumours

DEFF Research Database (Denmark)

Capper, David; Jones, David T.W.; Sill, Martin

2018-01-01

Accurate pathological diagnosis is crucial for optimal management of patients with cancer. For the approximately 100 known tumour types of the central nervous system, standardization of the diagnostic process has been shown to be particularly challenging - with substantial inter-observer variabil......Accurate pathological diagnosis is crucial for optimal management of patients with cancer. For the approximately 100 known tumour types of the central nervous system, standardization of the diagnostic process has been shown to be particularly challenging - with substantial inter......-observer variability in the histopathological diagnosis of many tumour types. Here we present a comprehensive approach for the DNA methylation-based classification of central nervous system tumours across all entities and age groups, and demonstrate its application in a routine diagnostic setting. We show...

Bond graphs for modelling, control and fault diagnosis of engineering systems

CERN Document Server

2017-01-01

This book presents theory and latest application work in Bond Graph methodology with a focus on: • Hybrid dynamical system models, • Model-based fault diagnosis, model-based fault tolerant control, fault prognosis • and also addresses • Open thermodynamic systems with compressible fluid flow, • Distributed parameter models of mechanical subsystems. In addition, the book covers various applications of current interest ranging from motorised wheelchairs, in-vivo surgery robots, walking machines to wind-turbines.The up-to-date presentation has been made possible by experts who are active members of the worldwide bond graph modelling community. This book is the completely revised 2nd edition of the 2011 Springer compilation text titled Bond Graph Modelling of Engineering Systems – Theory, Applications and Software Support. It extends the presentation of theory and applications of graph methodology by new developments and latest research results. Like the first edition, this book addresses readers in a...

A preliminary study of breast cancer diagnosis using laboratory based small angle x-ray scattering

Science.gov (United States)

Round, A. R.; Wilkinson, S. J.; Hall, C. J.; Rogers, K. D.; Glatter, O.; Wess, T.; Ellis, I. O.

2005-09-01

Breast tissue collected from tumour samples and normal tissue from bi-lateral mastectomy procedures were examined using small angle x-ray scattering. Previous work has indicated that breast tissue disease diagnosis could be performed using small angle x-ray scattering (SAXS) from a synchrotron radiation source. The technique would be more useful to health services if it could be made to work using a conventional x-ray source. Consistent and reliable differences in x-ray scatter distributions were observed between samples from normal and tumour tissue samples using the laboratory based 'SAXSess' system. Albeit from a small number of samples, a sensitivity of 100% was obtained. This result encourages us to pursue the implementation of SAXS as a laboratory based diagnosis technique.

A preliminary study of breast cancer diagnosis using laboratory based small angle x-ray scattering

Energy Technology Data Exchange (ETDEWEB)

Round, A R [Daresbury Laboratories, Warrington, WA4 4AD (United Kingdom); Wilkinson, S J [Daresbury Laboratories, Warrington, WA4 4AD (United Kingdom); Hall, C J [Daresbury Laboratories, Warrington, WA4 4AD (United Kingdom); Rogers, K D [Department of Materials and Medical Sciences, Cranfield University, Swindon, SN6 8LA (United Kingdom); Glatter, O [Department of Chemistry, University of Graz (Austria); Wess, T [School of Optometry and Vision Sciences, Cardiff University, Cardiff CF10 3NB, Wales (United Kingdom); Ellis, I O [Nottingham City Hospital, Nottingham (United Kingdom)

2005-09-07

Breast tissue collected from tumour samples and normal tissue from bi-lateral mastectomy procedures were examined using small angle x-ray scattering. Previous work has indicated that breast tissue disease diagnosis could be performed using small angle x-ray scattering (SAXS) from a synchrotron radiation source. The technique would be more useful to health services if it could be made to work using a conventional x-ray source. Consistent and reliable differences in x-ray scatter distributions were observed between samples from normal and tumour tissue samples using the laboratory based 'SAXSess' system. Albeit from a small number of samples, a sensitivity of 100% was obtained. This result encourages us to pursue the implementation of SAXS as a laboratory based diagnosis technique.

A preliminary study of breast cancer diagnosis using laboratory based small angle x-ray scattering

International Nuclear Information System (INIS)

Round, A R; Wilkinson, S J; Hall, C J; Rogers, K D; Glatter, O; Wess, T; Ellis, I O

2005-01-01

Breast tissue collected from tumour samples and normal tissue from bi-lateral mastectomy procedures were examined using small angle x-ray scattering. Previous work has indicated that breast tissue disease diagnosis could be performed using small angle x-ray scattering (SAXS) from a synchrotron radiation source. The technique would be more useful to health services if it could be made to work using a conventional x-ray source. Consistent and reliable differences in x-ray scatter distributions were observed between samples from normal and tumour tissue samples using the laboratory based 'SAXSess' system. Albeit from a small number of samples, a sensitivity of 100% was obtained. This result encourages us to pursue the implementation of SAXS as a laboratory based diagnosis technique

An application of first-principles diagnosis to a thermalhydraulic system

International Nuclear Information System (INIS)

Lapointe, P.A.; Chung, J.

1990-03-01

Recent advances in computer technology, such as artificial intelligence and interactive multimedia, offer significant new opportunities to enhance nuclear plant safety and improve the performance of the operations staff. Atomic Energy of Canada Limited is developing a framework on which newer approaches to operator support systems will be implemented. A prototype system has been developed for plant information access and display, on-line advice and diagnosis, and interactive operating procedures; it is called the Operator Companion. This paper describes the work performed for the development of the fault detection and diagnostic module within the Operator Companion. An early prototype of the module was developed for a small heat transfer circuit. A qualitative physics representation coupled with first-principles diagnosis using constraint suspension was utilized. Temporal information was required; it was integrated into the diagnosis using a type of directed graph to record event dependencies. This graph dynamically altered the qualitative model to reflect changes in the system over time. Although not completely formal, out method has successfully integrated the time diagnostic information to permit the identification of the faulty components and limit the number of spurious candidates in the tests performed. This paper summarizes the qualitative diagnosis concepts, describes the special temporal reasoning scheme developed, and presents a summary of the results obtained

Development of an accident diagnosis system using a dynamic neural network for nuclear power plants

International Nuclear Information System (INIS)

Lee, Seung Jun; Kim, Jong Hyun; Seong, Poong Hyun

2004-01-01

In this work, an accident diagnosis system using the dynamic neural network is developed. In order to help the plant operators to quickly identify the problem, perform diagnosis and initiate recovery actions ensuring the safety of the plant, many operator support system and accident diagnosis systems have been developed. Neural networks have been recognized as a good method to implement an accident diagnosis system. However, conventional accident diagnosis systems that used neural networks did not consider a time factor sufficiently. If the neural network could be trained according to time, it is possible to perform more efficient and detailed accidents analysis. Therefore, this work suggests a dynamic neural network which has different features from existing dynamic neural networks. And a simple accident diagnosis system is implemented in order to validate the dynamic neural network. After training of the prototype, several accident diagnoses were performed. The results show that the prototype can detect the accidents correctly with good performances

Gene-Based Multiclass Cancer Diagnosis with Class-Selective Rejections

Science.gov (United States)

Jrad, Nisrine; Grall-Maës, Edith; Beauseroy, Pierre

2009-01-01

Supervised learning of microarray data is receiving much attention in recent years. Multiclass cancer diagnosis, based on selected gene profiles, are used as adjunct of clinical diagnosis. However, supervised diagnosis may hinder patient care, add expense or confound a result. To avoid this misleading, a multiclass cancer diagnosis with class-selective rejection is proposed. It rejects some patients from one, some, or all classes in order to ensure a higher reliability while reducing time and expense costs. Moreover, this classifier takes into account asymmetric penalties dependant on each class and on each wrong or partially correct decision. It is based on ν-1-SVM coupled with its regularization path and minimizes a general loss function defined in the class-selective rejection scheme. The state of art multiclass algorithms can be considered as a particular case of the proposed algorithm where the number of decisions is given by the classes and the loss function is defined by the Bayesian risk. Two experiments are carried out in the Bayesian and the class selective rejection frameworks. Five genes selected datasets are used to assess the performance of the proposed method. Results are discussed and accuracies are compared with those computed by the Naive Bayes, Nearest Neighbor, Linear Perceptron, Multilayer Perceptron, and Support Vector Machines classifiers. PMID:19584932

Nonparametric method for failures diagnosis in the actuating subsystem of aircraft control system

Science.gov (United States)

Terentev, M. N.; Karpenko, S. S.; Zybin, E. Yu; Kosyanchuk, V. V.

2018-02-01

In this paper we design a nonparametric method for failures diagnosis in the aircraft control system that uses the measurements of the control signals and the aircraft states only. It doesn’t require a priori information of the aircraft model parameters, training or statistical calculations, and is based on analytical nonparametric one-step-ahead state prediction approach. This makes it possible to predict the behavior of unidentified and failure dynamic systems, to weaken the requirements to control signals, and to reduce the diagnostic time and problem complexity.

An MRI-based diagnostic framework for early diagnosis of dyslexia

International Nuclear Information System (INIS)

El-Baz, A.; Casanova, M.; Mott, M.; Switala, A.; Gimel'farb, G.

2008-01-01

A computer-aided diagnosis (CAD) system for early diagnosis of dyslexia was developed and tested. Dyslexia can severely impair the learning abilities of children so improved diagnostic methods are needed. Neuropathological studies show abnormal anatomy of the cerebral white matter (CWM) in dyslexic brains. We sought to develop an MRI-based macroscopic neuropathological correlate to the minicolumnopathy of dyslexia that relates to cortical connectivity: the gyral window. The brains of dyslexic patients often exhibit decreased gyrifications, so the thickness of gyral CWM for dyslexic subjects is greater than for normal subjects. We developed an MRI-based method for assessment of gyral CWM thickness with automated recognition of abnormal (e.g., dyslexic) brains. In vivo data was collected from 16 right-handed dyslexic men aged 18-40 years, and a group of 14 controls matched for gender, age, educational level, socioeconomic background, handedness and general intelligence. All the subjects were physically healthy and free of history of neurological diseases and head injury. Images were acquired with the same 1.5T MRI scanner (GE, Milwaukee, WI, USA) with voxel resolution 0.9375 x 0.9375 x 1.5 mm using a T1-weighted imaging sequence protocol. The ''ground truth'' diagnosis to evaluate the classification accuracy for each patient was given by the clinicians. The accuracy of diagnosis/classification of both the training and test subjects was evaluated using the Chi-square test at the three confidence levels - 85, 90 and 95% - in order to examine significant differences in the Levy distances. As expected, the 85% confidence level yielded the best results, the system correctly classified 16 out of 16 dyslexic subjects (a 100% accuracy) and 14 out of 14 control subjects (a 100% accuracy). At the 90% confidence level, 16 out of 16 dyslexic subjects were still classified correctly; however, only 13 out of 14 control subjects were correct, bringing the accuracy rate for the

An MRI-based diagnostic framework for early diagnosis of dyslexia

Energy Technology Data Exchange (ETDEWEB)

El-Baz, A. [University of Louisville, Bioengineering Department, Louisville, KY (United States); Casanova, M.; Mott, M.; Switala, A. [University of Louisville, Department of Psychiatry and Behavioral Science, Louisville, KY (United States); Gimel' farb, G. [University of Auckland, Computer Science Department, Auckland (New Zealand)

2008-09-15

A computer-aided diagnosis (CAD) system for early diagnosis of dyslexia was developed and tested. Dyslexia can severely impair the learning abilities of children so improved diagnostic methods are needed. Neuropathological studies show abnormal anatomy of the cerebral white matter (CWM) in dyslexic brains. We sought to develop an MRI-based macroscopic neuropathological correlate to the minicolumnopathy of dyslexia that relates to cortical connectivity: the gyral window. The brains of dyslexic patients often exhibit decreased gyrifications, so the thickness of gyral CWM for dyslexic subjects is greater than for normal subjects. We developed an MRI-based method for assessment of gyral CWM thickness with automated recognition of abnormal (e.g., dyslexic) brains. In vivo data was collected from 16 right-handed dyslexic men aged 18-40 years, and a group of 14 controls matched for gender, age, educational level, socioeconomic background, handedness and general intelligence. All the subjects were physically healthy and free of history of neurological diseases and head injury. Images were acquired with the same 1.5T MRI scanner (GE, Milwaukee, WI, USA) with voxel resolution 0.9375 x 0.9375 x 1.5 mm using a T1-weighted imaging sequence protocol. The ''ground truth'' diagnosis to evaluate the classification accuracy for each patient was given by the clinicians. The accuracy of diagnosis/classification of both the training and test subjects was evaluated using the Chi-square test at the three confidence levels - 85, 90 and 95% - in order to examine significant differences in the Levy distances. As expected, the 85% confidence level yielded the best results, the system correctly classified 16 out of 16 dyslexic subjects (a 100% accuracy) and 14 out of 14 control subjects (a 100% accuracy). At the 90% confidence level, 16 out of 16 dyslexic subjects were still classified correctly; however, only 13 out of 14 control subjects were correct, bringing the

Track Circuit Fault Diagnosis Method based on Least Squares Support Vector

Science.gov (United States)

Cao, Yan; Sun, Fengru

2018-01-01

In order to improve the troubleshooting efficiency and accuracy of the track circuit, track circuit fault diagnosis method was researched. Firstly, the least squares support vector machine was applied to design the multi-fault classifier of the track circuit, and then the measured track data as training samples was used to verify the feasibility of the methods. Finally, the results based on BP neural network fault diagnosis methods and the methods used in this paper were compared. Results shows that the track fault classifier based on least squares support vector machine can effectively achieve the five track circuit fault diagnosis with less computing time.

Failure diagnosis and fault tree analysis

International Nuclear Information System (INIS)

Weber, G.

1982-07-01

In this report a methodology of failure diagnosis for complex systems is presented. Systems which can be represented by fault trees are considered. This methodology is based on switching algebra, failure diagnosis of digital circuits and fault tree analysis. Relations between these disciplines are shown. These relations are due to Boolean algebra and Boolean functions used throughout. It will be shown on this basis that techniques of failure diagnosis and fault tree analysis are useful to solve the following problems: 1. describe an efficient search of all failed components if the system is failed. 2. Describe an efficient search of all states which are close to a system failure if the system is still operating. The first technique will improve the availability, the second the reliability and safety. For these problems, the relation to methods of failure diagnosis for combinational circuits is required. Moreover, the techniques are demonstrated for a number of systems which can be represented by fault trees. (orig./RW) [de

A belief network approach for development of a nuclear power plant diagnosis system

Energy Technology Data Exchange (ETDEWEB)

Hwang, I K; Kim, J T; Lee, D Y; Jung, C H; Kim, J Y; Lee, J S; Ham, C S [Korea Atomic Energy Research Institute, Taejon (Korea, Republic of)

1999-12-31

Belief network (or Bayesian network) based on Bayes` rule in probabilistic theory can be applied to the reasoning of diagnostic system. This paper describes the basic theory of concept and feasibility of using the network for diagnosis of nuclear power plants. An example shows that the probabilities of root causes of a failure are calculated from the measured or believed evidences. 6 refs., 3 figs. (Author)

A belief network approach for development of a nuclear power plant diagnosis system

Energy Technology Data Exchange (ETDEWEB)

Hwang, I. K.; Kim, J. T.; Lee, D. Y.; Jung, C. H.; Kim, J. Y.; Lee, J. S.; Ham, C. S. [Korea Atomic Energy Research Institute, Taejon (Korea, Republic of)

1998-12-31

Belief network (or Bayesian network) based on Bayes` rule in probabilistic theory can be applied to the reasoning of diagnostic system. This paper describes the basic theory of concept and feasibility of using the network for diagnosis of nuclear power plants. An example shows that the probabilities of root causes of a failure are calculated from the measured or believed evidences. 6 refs., 3 figs. (Author)

DNA Sensors for Malaria Diagnosis

DEFF Research Database (Denmark)

Hede, Marianne Smedegaard; Fjelstrup, Søren; Knudsen, Birgitta R.

2015-01-01

In the field of malaria diagnosis much effort is put into the development of faster and easier alternatives to the gold standard, blood smear microscopy. Nucleic acid amplification based techniques pose some of the most promising upcoming diagnostic tools due to their potential for high sensitivity......, robustness and user-friendliness. In the current review, we will discuss some of the different DNA-based sensor systems under development for the diagnosis of malaria....

Expert System Diagnosis of Cataract Eyes Using Fuzzy Mamdani Method

Science.gov (United States)

Santosa, I.; Romla, L.; Herawati, S.

2018-01-01

Cataracts are eye diseases characterized by cloudy or opacity of the lens of the eye by changing the colour of black into grey-white which slowly continues to grow and develop without feeling pain and pain that can cause blindness in human vision. Therefore, researchers make an expert system of cataract eye disease diagnosis by using Fuzzy Mamdani and how to care. The fuzzy method can convert the crisp value to linguistic value by fuzzification and includes in the rule. So this system produces an application program that can help the public in knowing cataract eye disease and how to care based on the symptoms suffered. From the results of the design implementation and testing of expert system applications to diagnose eye disease cataracts, it can be concluded that from a trial of 50 cases of data, obtained test results accuracy between system predictions with expert predictions obtained a value of 78% truth.

Diagnostic performance and system delay using telemedicine for prehospital diagnosis in triaging and teatment of STEMI

DEFF Research Database (Denmark)

Rasmussen, Martin Bøhme; Frost, Lars; Stengaard, Carsten

2014-01-01

diagnoses established by telemedicine confirmed on hospital arrival, and we determined system delay in patients diagnosed before hospital arrival and triaged directly to the catheterisation laboratory. Methods: Design: Population-based follow-up study. Setting: Central Denmark Region. Participants: 15 992...... patients diagnosed using telemedicine. Results: During the study period, a tentative diagnosis of STEMI was established in 1061 patients, of whom 919 were triaged directly to the PCI centre. In 771 (84%) patients, a diagnosis of STEMI was confirmed. Patients transported ... living telemedicine for prehospital diagnosis and triage of patients directly to the catheter laboratory is feasible and allows 89% of patients living up to 95 km from the invasive centre to be treated...

Structural analysis for Diagnosis

DEFF Research Database (Denmark)

Izadi-Zamanabadi, Roozbeh; Blanke, M.

2001-01-01

Aiming at design of algorithms for fault diagnosis, structural analysis of systems offers concise yet easy overall analysis. Graph-based matching, which is the essential technique to obtain redundant information for diagnosis, is re-considered in this paper. Matching is re-formulated as a problem...... of relating faults to known parameters and measurements of a system. Using explicit fault modelling, minimal over-determined subsystems are shown to provide necessary redundancy relations from the matching. Details of the method are presented and a realistic example used to clearly describe individual steps...

Structural analysis for diagnosis

DEFF Research Database (Denmark)

Izadi-Zamanabadi, Roozbeh; Blanke, M.

2002-01-01

Aiming at design of algorithms for fault diagnosis, structural analysis of systems offers concise yet easy overall analysis. Graph-based matching, which is the essential tech-nique to obtain redundant information for diagnosis, is reconsidered in this paper. Matching is reformulated as a problem...... of relating faults to known parameters and measurements of a system. Using explicit fault modelling, minimal overdetermined subsystems are shown to provide necessary redundancy relations from the matching. Details of the method are presented and a realistic example used to clearly describe individual steps....

Verification test for on-line diagnosis algorithm based on noise analysis

International Nuclear Information System (INIS)

Tamaoki, T.; Naito, N.; Tsunoda, T.; Sato, M.; Kameda, A.

1980-01-01

An on-line diagnosis algorithm was developed and its verification test was performed using a minicomputer. This algorithm identifies the plant state by analyzing various system noise patterns, such as power spectral densities, coherence functions etc., in three procedure steps. Each obtained noise pattern is examined by using the distances from its reference patterns prepared for various plant states. Then, the plant state is identified by synthesizing each result with an evaluation weight. This weight is determined automatically from the reference noise patterns prior to on-line diagnosis. The test was performed with 50 MW (th) Steam Generator noise data recorded under various controller parameter values. The algorithm performance was evaluated based on a newly devised index. The results obtained with one kind of weight showed the algorithm efficiency under the proper selection of noise patterns. Results for another kind of weight showed the robustness of the algorithm to this selection. (orig.)

Metabolic profile at first-time schizophrenia diagnosis: a population-based cross-sectional study

Directory of Open Access Journals (Sweden)

Horsdal HT

2017-02-01

Full Text Available Henriette Thisted Horsdal,1,2 Michael Eriksen Benros,2,3 Ole Köhler-Forsberg,2–4 Jesper Krogh,3 Christiane Gasse1,2,5 1National Centre for Register-based Research, Department of Economics and Business Economics, Aarhus BSS, Aarhus University, Aarhus, 2The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, 3Faculty of Health Sciences, Mental Health Centre Copenhagen, University of Copenhagen, Copenhagen, 4Psychosis Research Unit, Aarhus University Hospital, Risskov, 5Centre for Integrated Register-Based Research, Aarhus University, Aarhus, Denmark Objective: Schizophrenia and/or antipsychotic drug use are associated with metabolic abnormalities; however, knowledge regarding metabolic status and physician’s monitoring of metabolic status at first schizophrenia diagnosis is sparse. We assessed the prevalence of monitoring for metabolic blood abnormalities and characterized the metabolic profiles in people with a first-time schizophrenia diagnosis. Methods: This is a population-based cross-sectional study including all adults born in Denmark after January 1, 1955, with their first schizophrenia diagnosis between 2000 and 2012 in the Central Denmark Region. Information on metabolic parameters was obtained from a clinical laboratory information system. Associations were calculated using Wilcoxon rank-sum tests, chi-square tests, logistic regression, and Spearman’s correlation coefficients. Results: A total of 2,452 people with a first-time schizophrenia diagnosis were identified, of whom 1,040 (42.4% were monitored for metabolic abnormalities. Among those monitored, 58.4% had an abnormal lipid profile and 13.8% had an abnormal glucose profile. People who had previously filled prescription(s for antipsychotic drugs were more likely to present an abnormal lipid measure (65.7% vs 46.8%, P<0.001 and abnormal glucose profile (16.4% vs 10.1%, P=0.01. Conclusion: Metabolic abnormalities are common at first

Control and fault diagnosis based sliding mode observer of a multicellular converter: Hybrid approach

KAUST Repository

Benzineb, Omar

2013-01-01

In this article, the diagnosis of a three cell converter is developed. The hybrid nature of the system represented by the presence of continuous and discrete dynamics is taken into account in the control design. The idea is based on using a hybrid control and an observer-type sliding mode to generate residuals from the observation errors of the system. The simulation results are presented at the end to illustrate the performance of the proposed approach. © 2013 FEI STU.

Diagnosis abnormalities of limb movement in disorders of the nervous system

Science.gov (United States)

Tymchik, Gregory S.; Skytsiouk, Volodymyr I.; Klotchko, Tatiana R.; Bezsmertna, Halyna; Wójcik, Waldemar; Luganskaya, Saule; Orazbekov, Zhassulan; Iskakova, Aigul

2017-08-01

The paper deals with important issues of diagnosis early signs of diseases of the nervous system, including Parkinson's disease and other specific diseases. Small quantities of violation trajectory of spatial movement of the extremities of human disease at the primary level as the most appropriate features are studied. In modern medical practice is very actual the control the emergence of diseases of the nervous system, including Parkinson's disease. In work a model limbs with six rotational kinematic pairs for diagnosis of early signs of diseases of the nervous system is considered. subject.

miR-221 suppression through nanoparticle-based miRNA delivery system for hepatocellular carcinoma therapy and its diagnosis as a potential biomarker.

Science.gov (United States)

Li, Feng; Wang, Feiran; Zhu, Changlai; Wei, Qun; Zhang, Tianyi; Zhou, You Lang

2018-01-01

MicroRNA-221(miR-221) is frequently dysregulated in cancer. The purpose of this study was to explore whether miR-221 can be used as a potential diagnostic marker or therapeutic target for hepatocellular carcinoma (HCC). In this study, we investigated whether miR-221 expression was associated with clini-copathological characteristics and prognosis in HCC patients, and we developed a nanoparticle-based miRNA delivery system and detected its therapeutic efficacy in vitro and in vivo. We found that miR-221 was upregulated in HCC tissues, cell lines and blood of HCC patients. Upregulated miR-221 was associated with clinical TNM stage and tumor capsular infiltration, and showed poor prognosis, suggesting that its suppression could serve as an effective approach for hepatocellular carcinoma therapy. Treatment of HCC cells with nanoparticle/miR-221 inhibitor complexes suppressed their growth, colony formation ability, migration and invasion. In vivo, the growth of the tumors treated by the nanoparticle/miR-221 inhibitor complexes were significantly less than those treated by the nanoparticle/miRNA scramble complexes. In addition, circulating miR-221 may act as a potential tumor biomarker for early diagnosis of HCC, and combined serum miR-221 and AFP detection gave a better performance than individual detection in early diagnosis of HCC. These findings suggest that a nanoparticle-based miRNA delivery system could potentially serve as a safe and effective treatment and miR-221 could also be a potential diagnostic marker for HCC.

General knowledge structure for diagnosis

International Nuclear Information System (INIS)

Steinar Brendeford, T.

1996-01-01

At the OECD Halden Reactor Project work has been going on for several years in the field of automatic fault diagnosis for nuclear power plants. Continuing this work, studies are now carried out to combine different diagnostic systems within the same framework. The goal is to establish a general knowledge structure for diagnosis applied to a NPP process. Such a consistent and generic storage of knowledge will lighten the task of combining different diagnosis techniques. An integration like this is expected to increase the robustness and widen the scope of the diagnosis. Further, verification of system reliability and on-line explanations of hypotheses can be helped. Last but not least there is a potential in reuse of both specific and generic knowledge. The general knowledge framework is also a prerequisite for a successful integration of computerized operator support systems within the process supervision and control complex. Consistency, verification and reuse are keywords also in this respect. Systems that should be considered for integration are; automatic control, computerized operator procedures, alarm - and alarm filtering, signal validation, diagnosis and condition based maintenance. This paper presents three prototype diagnosis systems developed at the OECD Halden Reactor Project. A software arrangement for process simulation with these three systems attached in parallel is briefly described. The central part of this setup is a 'blackboard' system to be used for representing shared knowledge. Examples of such knowledge representations are included in the paper. The conclusions so far in this line of work are only tentative. The studies of existing methodologies for diagnosis, however, show a potential for several generalizations to be made in knowledge representation and use. (author). 14 refs, 6 figs

General knowledge structure for diagnosis

Energy Technology Data Exchange (ETDEWEB)

Steinar Brendeford, T [Institutt for Energiteknikk, Halden (Norway). OECD Halden Reaktor Projekt

1997-12-31

At the OECD Halden Reactor Project work has been going on for several years in the field of automatic fault diagnosis for nuclear power plants. Continuing this work, studies are now carried out to combine different diagnostic systems within the same framework. The goal is to establish a general knowledge structure for diagnosis applied to a NPP process. Such a consistent and generic storage of knowledge will lighten the task of combining different diagnosis techniques. An integration like this is expected to increase the robustness and widen the scope of the diagnosis. Further, verification of system reliability and on-line explanations of hypotheses can be helped. Last but not least there is a potential in reuse of both specific and generic knowledge. The general knowledge framework is also a prerequisite for a successful integration of computerized operator support systems within the process supervision and control complex. Consistency, verification and reuse are keywords also in this respect. Systems that should be considered for integration are; automatic control, computerized operator procedures, alarm - and alarm filtering, signal validation, diagnosis and condition based maintenance. This paper presents three prototype diagnosis systems developed at the OECD Halden Reactor Project. A software arrangement for process simulation with these three systems attached in parallel is briefly described. The central part of this setup is a `blackboard` system to be used for representing shared knowledge. Examples of such knowledge representations are included in the paper. The conclusions so far in this line of work are only tentative. The studies of existing methodologies for diagnosis, however, show a potential for several generalizations to be made in knowledge representation and use. (author). 14 refs, 6 figs.

An expert system approach for safety diagnosis

International Nuclear Information System (INIS)

Erdmann, R.C.; Sun, B.K.H.

1988-01-01

An expert system was developed with the intent to provide real-time information about an accident to an operator who is in the process of diagnosing and bringing that accident under control. Explicit use was made of probabilistic risk analysis techniques and plant accident response information in constructing this system. The expert system developed contains 70 logic rules and provides contextual messages during simulated accident sequences and logic sequence information on the entire sequence in graphical form for accident diagnosis. The present analysis focuses on integrated control system-related transients with Babcock and Wilcox-type reactors. While the system developed here is limited in extent and was built for a composite reactor, it demonstrates that an expert system may enhance the operator's capability in the control room

Fault Diagnosis for Rotating Machinery: A Method based on Image Processing.

Directory of Open Access Journals (Sweden)

Chen Lu

Full Text Available Rotating machinery is one of the most typical types of mechanical equipment and plays a significant role in industrial applications. Condition monitoring and fault diagnosis of rotating machinery has gained wide attention for its significance in preventing catastrophic accident and guaranteeing sufficient maintenance. With the development of science and technology, fault diagnosis methods based on multi-disciplines are becoming the focus in the field of fault diagnosis of rotating machinery. This paper presents a multi-discipline method based on image-processing for fault diagnosis of rotating machinery. Different from traditional analysis method in one-dimensional space, this study employs computing method in the field of image processing to realize automatic feature extraction and fault diagnosis in a two-dimensional space. The proposed method mainly includes the following steps. First, the vibration signal is transformed into a bi-spectrum contour map utilizing bi-spectrum technology, which provides a basis for the following image-based feature extraction. Then, an emerging approach in the field of image processing for feature extraction, speeded-up robust features, is employed to automatically exact fault features from the transformed bi-spectrum contour map and finally form a high-dimensional feature vector. To reduce the dimensionality of the feature vector, thus highlighting main fault features and reducing subsequent computing resources, t-Distributed Stochastic Neighbor Embedding is adopt to reduce the dimensionality of the feature vector. At last, probabilistic neural network is introduced for fault identification. Two typical rotating machinery, axial piston hydraulic pump and self-priming centrifugal pumps, are selected to demonstrate the effectiveness of the proposed method. Results show that the proposed method based on image-processing achieves a high accuracy, thus providing a highly effective means to fault diagnosis for

Fault Diagnosis for Rotating Machinery: A Method based on Image Processing.

Science.gov (United States)

Lu, Chen; Wang, Yang; Ragulskis, Minvydas; Cheng, Yujie

2016-01-01

Rotating machinery is one of the most typical types of mechanical equipment and plays a significant role in industrial applications. Condition monitoring and fault diagnosis of rotating machinery has gained wide attention for its significance in preventing catastrophic accident and guaranteeing sufficient maintenance. With the development of science and technology, fault diagnosis methods based on multi-disciplines are becoming the focus in the field of fault diagnosis of rotating machinery. This paper presents a multi-discipline method based on image-processing for fault diagnosis of rotating machinery. Different from traditional analysis method in one-dimensional space, this study employs computing method in the field of image processing to realize automatic feature extraction and fault diagnosis in a two-dimensional space. The proposed method mainly includes the following steps. First, the vibration signal is transformed into a bi-spectrum contour map utilizing bi-spectrum technology, which provides a basis for the following image-based feature extraction. Then, an emerging approach in the field of image processing for feature extraction, speeded-up robust features, is employed to automatically exact fault features from the transformed bi-spectrum contour map and finally form a high-dimensional feature vector. To reduce the dimensionality of the feature vector, thus highlighting main fault features and reducing subsequent computing resources, t-Distributed Stochastic Neighbor Embedding is adopt to reduce the dimensionality of the feature vector. At last, probabilistic neural network is introduced for fault identification. Two typical rotating machinery, axial piston hydraulic pump and self-priming centrifugal pumps, are selected to demonstrate the effectiveness of the proposed method. Results show that the proposed method based on image-processing achieves a high accuracy, thus providing a highly effective means to fault diagnosis for rotating machinery.

Fault diagnosis based on controller modification

DEFF Research Database (Denmark)

Niemann, Hans Henrik

2015-01-01

Detection and isolation of parametric faults in closed-loop systems will be considered in this paper. A major problem is that a feedback controller will in general reduce the effects from variations in the systems including parametric faults on the controlled output from the system. Parametric...... faults can be detected and isolated using active methods, where an auxiliary input is applied. Using active methods for the diagnosis of parametric faults in closed-loop systems, the amplitude of the applied auxiliary input need to be increased to be able to detect and isolate the faults in a reasonable......-parameterization (after Youla, Jabr, Bongiorno and Kucera) for the controller, it is possible to modify the feedback controller with a minor effect on the closed-loop performance in the fault-free case and at the same time optimize the detection and isolation in a faulty case. Controller modification in connection...

Knowledge-Based Systems in Biomedicine and Computational Life Science

CERN Document Server

Jain, Lakhmi

2013-01-01

This book presents a sample of research on knowledge-based systems in biomedicine and computational life science. The contributions include: ·         personalized stress diagnosis system ·         image analysis system for breast cancer diagnosis ·         analysis of neuronal cell images ·         structure prediction of protein ·         relationship between two mental disorders ·         detection of cardiac abnormalities ·         holistic medicine based treatment ·         analysis of life-science data  

A Modular Neural Network Scheme Applied to Fault Diagnosis in Electric Power Systems

Directory of Open Access Journals (Sweden)

Agustín Flores

2014-01-01

Full Text Available This work proposes a new method for fault diagnosis in electric power systems based on neural modules. With this method the diagnosis is performed by assigning a neural module for each type of component comprising the electric power system, whether it is a transmission line, bus or transformer. The neural modules for buses and transformers comprise two diagnostic levels which take into consideration the logic states of switches and relays, both internal and back-up, with the exception of the neural module for transmission lines which also has a third diagnostic level which takes into account the oscillograms of fault voltages and currents as well as the frequency spectrums of these oscillograms, in order to verify if the transmission line had in fact been subjected to a fault. One important advantage of the diagnostic system proposed is that its implementation does not require the use of a network configurator for the system; it does not depend on the size of the power network nor does it require retraining of the neural modules if the power network increases in size, making its application possible to only one component, a specific area, or the whole context of the power system.

A setup for active fault diagnosis

DEFF Research Database (Denmark)

Niemann, Hans Henrik

2006-01-01

A setup for active fault diagnosis (AFD) of parametric faults in dynamic systems is formulated in this paper. It is shown that it is possible to use the same setup for both open loop systems, closed loop systems based on a nominal feedback controller as well as for closed loop systems based...... on a reconfigured feedback controller. This will make the proposed AFD approach very useful in connection with fault tolerant control (FTC). The setup will make it possible to let the fault diagnosis part of the fault tolerant controller remain unchanged after a change in the feedback controller. The setup for AFD...... is based on the YJBK (after Youla, Jabr, Bongiorno and Kucera) parameterization of all stabilizing feedback controllers and the dual YJBK parameterization. It is shown that the AFD is based directly on the dual YJBK transfer function matrix. This matrix will be named the fault signature matrix when...

DNA methylation-based classification of central nervous system tumours.

Science.gov (United States)

Capper, David; Jones, David T W; Sill, Martin; Hovestadt, Volker; Schrimpf, Daniel; Sturm, Dominik; Koelsche, Christian; Sahm, Felix; Chavez, Lukas; Reuss, David E; Kratz, Annekathrin; Wefers, Annika K; Huang, Kristin; Pajtler, Kristian W; Schweizer, Leonille; Stichel, Damian; Olar, Adriana; Engel, Nils W; Lindenberg, Kerstin; Harter, Patrick N; Braczynski, Anne K; Plate, Karl H; Dohmen, Hildegard; Garvalov, Boyan K; Coras, Roland; Hölsken, Annett; Hewer, Ekkehard; Bewerunge-Hudler, Melanie; Schick, Matthias; Fischer, Roger; Beschorner, Rudi; Schittenhelm, Jens; Staszewski, Ori; Wani, Khalida; Varlet, Pascale; Pages, Melanie; Temming, Petra; Lohmann, Dietmar; Selt, Florian; Witt, Hendrik; Milde, Till; Witt, Olaf; Aronica, Eleonora; Giangaspero, Felice; Rushing, Elisabeth; Scheurlen, Wolfram; Geisenberger, Christoph; Rodriguez, Fausto J; Becker, Albert; Preusser, Matthias; Haberler, Christine; Bjerkvig, Rolf; Cryan, Jane; Farrell, Michael; Deckert, Martina; Hench, Jürgen; Frank, Stephan; Serrano, Jonathan; Kannan, Kasthuri; Tsirigos, Aristotelis; Brück, Wolfgang; Hofer, Silvia; Brehmer, Stefanie; Seiz-Rosenhagen, Marcel; Hänggi, Daniel; Hans, Volkmar; Rozsnoki, Stephanie; Hansford, Jordan R; Kohlhof, Patricia; Kristensen, Bjarne W; Lechner, Matt; Lopes, Beatriz; Mawrin, Christian; Ketter, Ralf; Kulozik, Andreas; Khatib, Ziad; Heppner, Frank; Koch, Arend; Jouvet, Anne; Keohane, Catherine; Mühleisen, Helmut; Mueller, Wolf; Pohl, Ute; Prinz, Marco; Benner, Axel; Zapatka, Marc; Gottardo, Nicholas G; Driever, Pablo Hernáiz; Kramm, Christof M; Müller, Hermann L; Rutkowski, Stefan; von Hoff, Katja; Frühwald, Michael C; Gnekow, Astrid; Fleischhack, Gudrun; Tippelt, Stephan; Calaminus, Gabriele; Monoranu, Camelia-Maria; Perry, Arie; Jones, Chris; Jacques, Thomas S; Radlwimmer, Bernhard; Gessi, Marco; Pietsch, Torsten; Schramm, Johannes; Schackert, Gabriele; Westphal, Manfred; Reifenberger, Guido; Wesseling, Pieter; Weller, Michael; Collins, Vincent Peter; Blümcke, Ingmar; Bendszus, Martin; Debus, Jürgen; Huang, Annie; Jabado, Nada; Northcott, Paul A; Paulus, Werner; Gajjar, Amar; Robinson, Giles W; Taylor, Michael D; Jaunmuktane, Zane; Ryzhova, Marina; Platten, Michael; Unterberg, Andreas; Wick, Wolfgang; Karajannis, Matthias A; Mittelbronn, Michel; Acker, Till; Hartmann, Christian; Aldape, Kenneth; Schüller, Ulrich; Buslei, Rolf; Lichter, Peter; Kool, Marcel; Herold-Mende, Christel; Ellison, David W; Hasselblatt, Martin; Snuderl, Matija; Brandner, Sebastian; Korshunov, Andrey; von Deimling, Andreas; Pfister, Stefan M

2018-03-22

Accurate pathological diagnosis is crucial for optimal management of patients with cancer. For the approximately 100 known tumour types of the central nervous system, standardization of the diagnostic process has been shown to be particularly challenging-with substantial inter-observer variability in the histopathological diagnosis of many tumour types. Here we present a comprehensive approach for the DNA methylation-based classification of central nervous system tumours across all entities and age groups, and demonstrate its application in a routine diagnostic setting. We show that the availability of this method may have a substantial impact on diagnostic precision compared to standard methods, resulting in a change of diagnosis in up to 12% of prospective cases. For broader accessibility, we have designed a free online classifier tool, the use of which does not require any additional onsite data processing. Our results provide a blueprint for the generation of machine-learning-based tumour classifiers across other cancer entities, with the potential to fundamentally transform tumour pathology.

Residual life of technical systems; diagnosis, prediction and life extension

International Nuclear Information System (INIS)

Reinertsen, Rune

1996-01-01

The paper presents and discusses research related to residual life of non-repairable and repairable technical systems. Diagnosis of systems and extension of residual life of technical systems are also presented and discussed. This paper concludes that research published describing determination and extension of residual life as well as methods for diagnosis of non-repairable and repairable technical systems, is somewhat limited. Many papers have a rather pragmatic approach. The authors only describe special cases from their own plant and do not provide any explanation of a more academical nature. The other papers are mainly describing very specific applications of statistical models, leaving the more general case out of consideration. One of the main results of this paper is to point out these facts, and thereby identify the need for future research in this area

Decision support system for the diagnosis of schizophrenia disorders

Directory of Open Access Journals (Sweden)

D. Razzouk

2006-01-01

Full Text Available Clinical decision support systems are useful tools for assisting physicians to diagnose complex illnesses. Schizophrenia is a complex, heterogeneous and incapacitating mental disorder that should be detected as early as possible to avoid a most serious outcome. These artificial intelligence systems might be useful in the early detection of schizophrenia disorder. The objective of the present study was to describe the development of such a clinical decision support system for the diagnosis of schizophrenia spectrum disorders (SADDESQ. The development of this system is described in four stages: knowledge acquisition, knowledge organization, the development of a computer-assisted model, and the evaluation of the system's performance. The knowledge was extracted from an expert through open interviews. These interviews aimed to explore the expert's diagnostic decision-making process for the diagnosis of schizophrenia. A graph methodology was employed to identify the elements involved in the reasoning process. Knowledge was first organized and modeled by means of algorithms and then transferred to a computational model created by the covering approach. The performance assessment involved the comparison of the diagnoses of 38 clinical vignettes between an expert and the SADDESQ. The results showed a relatively low rate of misclassification (18-34% and a good performance by SADDESQ in the diagnosis of schizophrenia, with an accuracy of 66-82%. The accuracy was higher when schizophreniform disorder was considered as the presence of schizophrenia disorder. Although these results are preliminary, the SADDESQ has exhibited a satisfactory performance, which needs to be further evaluated within a clinical setting.

The Relationship of Learning and Performance Diagnosis at Different System Levels.

Science.gov (United States)

Lubega, Khalid

2003-01-01

Examines learning and performance diagnosis, separately and in relation to each other, as they function in organization systems; explains the relationship between learning and performance diagnosis at the individual, process, and organizational levels using a three-level performance model; and discusses types of learning, including nonlearning,…

Representation and Use of Knowledge in Automatic Fault Diagnosis

International Nuclear Information System (INIS)

Brendeford, Tor S.

1996-01-01

The report relates activities performed within the ongoing project on Integrated Diagnosis Systems (IDS). A unifying aspect of the activities is representation of knowledge applied in diagnosis. New ways of representing such knowledge can improve the diagnoses, enable reuse, and facilitate consistent integration with other operator support systems. The tasks of the diagnostic process, and the roles of domain knowledge, are discussed in relation to different methods of diagnosis. Two primary methods of diagnosis are recognised in the report, model-based and association-based. Distinct differences of these two methods are identified as focus for integration. A methodology for specifying the design of diagnosis systems is reviewed. This methodology seems to provide a good theoretical basis for understanding problems of fault diagnosis. Qualitative and functional modelling methods are studied by application to a common example domain. The two specific techniques are found to be promising in relation to diagnosis. A software setup for simulated diagnosis is presented. This setup is to be used in the activity on knowledge representation, where a blackboard system is the central module of the setup. Presentations of process domain knowledge show the capabilities of the blackboard architecture and suggest schemes for integrated use of the information. The object-oriented architecture is also shown to serve the needs for presentation of diagnostic reasoning, which is a vital aspect when integrating different diagnosis methods. (author)

Bearing Fault Diagnosis Based on Statistical Locally Linear Embedding.

Science.gov (United States)

Wang, Xiang; Zheng, Yuan; Zhao, Zhenzhou; Wang, Jinping

2015-07-06

Fault diagnosis is essentially a kind of pattern recognition. The measured signal samples usually distribute on nonlinear low-dimensional manifolds embedded in the high-dimensional signal space, so how to implement feature extraction, dimensionality reduction and improve recognition performance is a crucial task. In this paper a novel machinery fault diagnosis approach based on a statistical locally linear embedding (S-LLE) algorithm which is an extension of LLE by exploiting the fault class label information is proposed. The fault diagnosis approach first extracts the intrinsic manifold features from the high-dimensional feature vectors which are obtained from vibration signals that feature extraction by time-domain, frequency-domain and empirical mode decomposition (EMD), and then translates the complex mode space into a salient low-dimensional feature space by the manifold learning algorithm S-LLE, which outperforms other feature reduction methods such as PCA, LDA and LLE. Finally in the feature reduction space pattern classification and fault diagnosis by classifier are carried out easily and rapidly. Rolling bearing fault signals are used to validate the proposed fault diagnosis approach. The results indicate that the proposed approach obviously improves the classification performance of fault pattern recognition and outperforms the other traditional approaches.

Sclerosing cholangitis: Clinicopathologic features, imaging spectrum, and systemic approach to differential diagnosis

Energy Technology Data Exchange (ETDEWEB)

Seo, Ni Eun [Dept. of Radiology, Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul (Korea, Republic of); Kim, So Yeon; Lee, Seung Soo; Byun, Jae Ho; Kim, Hyoung Jung; Kim, Jin Hee; Lee, Moon Gyu [Dept. of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul (Korea, Republic of)

2016-02-15

Sclerosing cholangitis is a spectrum of chronic progressive cholestatic liver disease characterized by inflammation, fibrosis, and stricture of the bile ducts, which can be classified as primary and secondary sclerosing cholangitis. Primary sclerosing cholangitis is a chronic progressive liver disease of unknown cause. On the other hand, secondary sclerosing cholangitis has identifiable causes that include immunoglobulin G4-related sclerosing disease, recurrent pyogenic cholangitis, ischemic cholangitis, acquired immunodeficiency syndrome-related cholangitis, and eosinophilic cholangitis. In this review, we suggest a systemic approach to the differential diagnosis of sclerosing cholangitis based on the clinical and laboratory findings, as well as the typical imaging features on computed tomography and magnetic resonance (MR) imaging with MR cholangiography. Familiarity with various etiologies of sclerosing cholangitis and awareness of their typical clinical and imaging findings are essential for an accurate diagnosis and appropriate management.

Sclerosing Cholangitis: Clinicopathologic Features, Imaging Spectrum, and Systemic Approach to Differential Diagnosis.

Science.gov (United States)

Seo, Nieun; Kim, So Yeon; Lee, Seung Soo; Byun, Jae Ho; Kim, Jin Hee; Kim, Hyoung Jung; Lee, Moon-Gyu

2016-01-01

Sclerosing cholangitis is a spectrum of chronic progressive cholestatic liver disease characterized by inflammation, fibrosis, and stricture of the bile ducts, which can be classified as primary and secondary sclerosing cholangitis. Primary sclerosing cholangitis is a chronic progressive liver disease of unknown cause. On the other hand, secondary sclerosing cholangitis has identifiable causes that include immunoglobulin G4-related sclerosing disease, recurrent pyogenic cholangitis, ischemic cholangitis, acquired immunodeficiency syndrome-related cholangitis, and eosinophilic cholangitis. In this review, we suggest a systemic approach to the differential diagnosis of sclerosing cholangitis based on the clinical and laboratory findings, as well as the typical imaging features on computed tomography and magnetic resonance (MR) imaging with MR cholangiography. Familiarity with various etiologies of sclerosing cholangitis and awareness of their typical clinical and imaging findings are essential for an accurate diagnosis and appropriate management.

Data-driven design of fault diagnosis and fault-tolerant control systems

CERN Document Server

Ding, Steven X

2014-01-01

Data-driven Design of Fault Diagnosis and Fault-tolerant Control Systems presents basic statistical process monitoring, fault diagnosis, and control methods, and introduces advanced data-driven schemes for the design of fault diagnosis and fault-tolerant control systems catering to the needs of dynamic industrial processes. With ever increasing demands for reliability, availability and safety in technical processes and assets, process monitoring and fault-tolerance have become important issues surrounding the design of automatic control systems. This text shows the reader how, thanks to the rapid development of information technology, key techniques of data-driven and statistical process monitoring and control can now become widely used in industrial practice to address these issues. To allow for self-contained study and facilitate implementation in real applications, important mathematical and control theoretical knowledge and tools are included in this book. Major schemes are presented in algorithm form and...

[Computer-aided Diagnosis and New Electronic Stethoscope].

Science.gov (United States)

Huang, Mei; Liu, Hongying; Pi, Xitian; Ao, Yilu; Wang, Zi

2017-05-30

Auscultation is an important method in early-diagnosis of cardiovascular disease and respiratory system disease. This paper presents a computer-aided diagnosis of new electronic auscultation system. It has developed an electronic stethoscope based on condenser microphone and the relevant intelligent analysis software. It has implemented many functions that combined with Bluetooth, OLED, SD card storage technologies, such as real-time heart and lung sounds auscultation in three modes, recording and playback, auscultation volume control, wireless transmission. The intelligent analysis software based on PC computer utilizes C# programming language and adopts SQL Server as the background database. It has realized play and waveform display of the auscultation sound. By calculating the heart rate, extracting the characteristic parameters of T1, T2, T12, T11, it can analyze whether the heart sound is normal, and then generate diagnosis report. Finally the auscultation sound and diagnosis report can be sent to mailbox of other doctors, which can carry out remote diagnosis. The whole system has features of fully function, high portability, good user experience, and it is beneficial to promote the use of electronic stethoscope in the hospital, at the same time, the system can also be applied to auscultate teaching and other occasions.

Novel fiber optic-based needle redox imager for cancer diagnosis

Science.gov (United States)

Kanniyappan, Udayakumar; Xu, He N.; Tang, Qinggong; Gaitan, Brandon; Liu, Yi; Li, Lin Z.; Chen, Yu

2018-02-01

Despite various technological advancements in cancer diagnosis, the mortality rates were not decreased significantly. We aim to develop a novel optical imaging tool to assist cancer diagnosis effectively. Fluorescence spectroscopy/imaging is a fast, rapid, and minimally invasive technique which has been successfully applied to diagnosing cancerous cells/tissues. Recently, the ratiometric imaging of intrinsic fluorescence of reduced nicotinamide adenine dinucleotide (NADH) and flavin adenine dinucleotide (FAD), as pioneered by Britton Chance and the co-workers in 1950-70's, has gained much attention to quantify the physiological parameters of living cells/tissues. The redox ratio, i.e., FAD/(FAD+NADH) or FAD/NADH, has been shown to be sensitive to various metabolic changes in in vivo and in vitro cells/tissues. Optical redox imaging has also been investigated for providing potential imaging biomarkers for cancer transformation, aggressiveness, and treatment response. Towards this goal, we have designed and developed a novel fiberoptic-based needle redox imager (NRI) that can fit into an 11G clinical coaxial biopsy needle for real time imaging during clinical cancer surgery. In the present study, the device is calibrated with tissue mimicking phantoms of FAD and NADH along with various technical parameters such as sensitivity, dynamic range, linearity, and spatial resolution of the system. We also conducted preliminary imaging of tissues ex vivo for validation. We plan to test the NRI on clinical breast cancer patients. Once validated this device may provide an effective tool for clinical cancer diagnosis.

Effect of diagnosis and treatment of clinical endometritis based on vaginal discharge score grading system in postpartum Holstein cows.

Science.gov (United States)

Okawa, Hiroaki; Fujikura, Atsushi; Wijayagunawardane, Missaka M P; Vos, Peter L A M; Taniguchi, Masayasu; Takagi, Mitsuhiro

2017-09-12

In this study, the prevalence, effectiveness of diagnosis, and treatment based on vaginal discharge score (VDS) of clinical endometritis in cattle were evaluated. To detect clinical endometritis and classify its severity, vaginoscopy was performed during 21 to 60 days postpartum in 164 Holstein cows consisting of 229 lactations. Groups were defined using the 4-point VDS scale. Study groups included the following: non-endometritis (VDS=0; no/clear mucus; NEM group; n=168); mild endometritis, no treatment (VDS=1; mucus containing flecks of white/off-white pus; NTR group; n=30); and severe endometritis, treated with PGF2α (VDS≥2; discharge containing 50% pus, and fluid or uterine horn asymmetry; TEM group; n=31). Cows treated with PGF2α that did not recover (VDS≥1, n=5) received intrauterine procaine penicillin and streptomycin. Prevalence of clinical endometritis (VDS≥1) was 26.6%. The NTR group required significantly more artificial inseminations per pregnancy than NEM and TEM groups (2.8 ± 1.8 vs 2.0 ± 1.3, 1.9 ± 0.8, Pcows was higher in the NTR group compared to the NEM (P=0.012) and TEM (P=0.076) groups. In the TEM group, calving to first artificial insemination interval tended to be higher in cows treated 41 to 60 days postpartum than cows treated 29 to 40 days postpartum (97.2 ± 27.1 vs 74.4 ± 19.7, P=0.084). Our study suggests that cows with VDS=1 may require treatment to recover fertility. Diagnosis and treatment of clinical endometritis based on a VDS grading system may improve dairy herd reproductive performance.

Wavelet-based information filtering for fault diagnosis of electric drive systems in electric ships.

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Silva, Andre A; Gupta, Shalabh; Bazzi, Ali M; Ulatowski, Arthur

2017-09-22

Electric machines and drives have enjoyed extensive applications in the field of electric vehicles (e.g., electric ships, boats, cars, and underwater vessels) due to their ease of scalability and wide range of operating conditions. This stems from their ability to generate the desired torque and power levels for propulsion under various external load conditions. However, as with the most electrical systems, the electric drives are prone to component failures that can degrade their performance, reduce the efficiency, and require expensive maintenance. Therefore, for safe and reliable operation of electric vehicles, there is a need for automated early diagnostics of critical failures such as broken rotor bars and electrical phase failures. In this regard, this paper presents a fault diagnosis methodology for electric drives in electric ships. This methodology utilizes the two-dimensional, i.e. scale-shift, wavelet transform of the sensor data to filter optimal information-rich regions which can enhance the diagnosis accuracy as well as reduce the computational complexity of the classifier. The methodology was tested on sensor data generated from an experimentally validated simulation model of electric drives under various cruising speed conditions. The results in comparison with other existing techniques show a high correct classification rate with low false alarm and miss detection rates. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Numerical model for thermoeconomic diagnosis in commercial transcritical/subcritical booster refrigeration systems

International Nuclear Information System (INIS)

Ommen, Torben; Elmegaard, Brian

2012-01-01

Highlights: ► A transcritical booster refrigeration plant is modelled. ► We examine changes in cost flow at different operation parameters. ► The use of characteristic curves for diagnosis is studied. - Abstract: Transcritical/subcritical booster refrigeration systems are increasingly installed and used in Danish supermarkets. The systems operate in both transcritical and subcritical conditions dependent on the heat rejection performance and the ambient conditions. The plant consists of one refrigerant cycle supplying refrigerant for evaporators in both chilled and frozen display cases. In the paper, thermoeconomic theory is used to establish the cost of cooling at each individual temperature level based on operating costs. With a high amount of operating systems, faulty operation becomes an economic, and environmental, interest. A general solution for evaluation of these systems is considered, with the objective to reduce cost and power consumption of malfunctioning equipment in operation. An analysis of the use of thermoeconomic diagnosis methods is required, as these methods may prove applicable. To accommodate the analysis, a numerical model of a transcritical booster refrigeration plant is considered in this paper. Additionally the characteristic curves method is applied to the high pressure compressor unit of the refrigeration plant. The approach successfully determine whether an anomaly is intrinsic or induced in the component when no uncertainties are introduced in the steady state model.

OPTICAL AND DIELECTRIC SENSORS BASED ON ANTIMICROBIAL PEPTIDES FOR MICROORGANISMS DIAGNOSIS

Directory of Open Access Journals (Sweden)

Rafael Ramos Silva

2014-08-01

Full Text Available Antimicrobial peptides (AMPs are natural compounds isolated from a wide variety of organisms that include microorganisms, insects, amphibians, plants and humans. These biomolecules are considered as part of the innate immune system and are known as natural antibiotics, presenting a broad spectrum of activities against bacteria, fungi and/or viruses. Technological innovations have enabled AMPs to be utilized for the development of novel biodetection devices. Advances in nanotechnology, such as the synthesis of nanocomposites, nanoparticles, and nanotubes have permitted the development of nanostructured platforms with biocompatibility and greater surface areas for the immobilization of biocomponents, arising as additional tools for obtaining more efficient biosensors. Diverse AMPs have been used as biological recognition elements for obtaining biosensors with more specificity and lower detection limits, whose analytical response can be evaluated through electrochemical impedance and fluorescence spectroscopies. AMP-based biosensors have shown potential for applications such as supplementary tools for conventional diagnosis methods of microorganisms. In this review, conventional methods for microorganism diagnosis as well new strategies using AMPs for the development of impedimetric and fluorescent biosensors are highlighted. AMP-based biosensors show promise as methods for diagnosing infections and bacterial contaminations as well as applications in quality control for clinical analyses and microbiological laboratories.

Resonance-Based Sparse Signal Decomposition and its Application in Mechanical Fault Diagnosis: A Review.

Science.gov (United States)

Huang, Wentao; Sun, Hongjian; Wang, Weijie

2017-06-03

Mechanical equipment is the heart of industry. For this reason, mechanical fault diagnosis has drawn considerable attention. In terms of the rich information hidden in fault vibration signals, the processing and analysis techniques of vibration signals have become a crucial research issue in the field of mechanical fault diagnosis. Based on the theory of sparse decomposition, Selesnick proposed a novel nonlinear signal processing method: resonance-based sparse signal decomposition (RSSD). Since being put forward, RSSD has become widely recognized, and many RSSD-based methods have been developed to guide mechanical fault diagnosis. This paper attempts to summarize and review the theoretical developments and application advances of RSSD in mechanical fault diagnosis, and to provide a more comprehensive reference for those interested in RSSD and mechanical fault diagnosis. Followed by a brief introduction of RSSD's theoretical foundation, based on different optimization directions, applications of RSSD in mechanical fault diagnosis are categorized into five aspects: original RSSD, parameter optimized RSSD, subband optimized RSSD, integrated optimized RSSD, and RSSD combined with other methods. On this basis, outstanding issues in current RSSD study are also pointed out, as well as corresponding instructional solutions. We hope this review will provide an insightful reference for researchers and readers who are interested in RSSD and mechanical fault diagnosis.

Method of fault diagnosis in nuclear power plant base on genetic algorithm and knowledge base

International Nuclear Information System (INIS)

Zhou Yangping; Zhao Bingquan

2000-01-01

Via using the knowledge base, combining Genetic Algorithm and classical probability and contraposing the characteristic of the fault diagnosis of NPP. The authors put forward a method of fault diagnosis. In the process of fault diagnosis, this method contact the state of NPP with the colony in GA and transform the colony to get the individual that adapts to the condition. On the 950MW full size simulator in Beijing NPP simulation training center, experimentation shows it has comparative adaptability to the imperfection of expert knowledge, illusive signal and other instance

Phronesis, a diagnosis and recovery tool for system administrators

International Nuclear Information System (INIS)

Haen, C; Barra, V; Bonaccorsi, E; Neufeld, N

2014-01-01

The LHCb experiment relies on the Online system, which includes a very large and heterogeneous computing cluster. Ensuring the proper behavior of the different tasks running on the more than 2000 servers represents a huge workload for the small operator team and is a 24/7 task. At CHEP 2012, we presented a prototype of a framework that we designed in order to support the experts. The main objective is to provide them with steadily improving diagnosis and recovery solutions in case of misbehavior of a service, without having to modify the original applications. Our framework is based on adapted principles of the Autonomic Computing model, on Reinforcement Learning algorithms, as well as innovative concepts such as Shared Experience. While the submission at CHEP 2012 showed the validity of our prototype on simulations, we here present an implementation with improved algorithms and manipulation tools, and report on the experience gained with running it in the LHCb Online system.

A system for tumor heterogeneity evaluation and diagnosis based on tumor markers measured routinely in the laboratory.

Science.gov (United States)

Hui, Liu; Rixv, Liu; Xiuying, Zhou

2015-12-01

To develop an efficient and reliable approach to estimate tumor heterogeneity and improve tumor diagnosis using multiple tumor markers measured routinely in the clinical laboratory. A total of 161 patients with different cancers were recruited as the cancer group, and 91 patients with non-oncological conditions were required as the non-oncological disease group. The control group comprised 90 randomly selected healthy subjects. AFP, CEA, CYFRA, CA125, CA153, CA199, CA724, and NSE levels were measured in all these subjects with a chemiluminescent microparticle immunoassay. The tumor marker with the maximum S/CO value (sample test value:cutoff value for discriminating individuals with and without tumors) was considered as a specific tumor marker (STM) for an individual. Tumor heterogeneity index (THI)=N/P (N: number of STMs; P: percentage of individuals with STMs in a certain tumor population) was used to quantify tumor heterogeneity: high THI indicated high tumor heterogeneity. The tumor marker index (TMI), TMI = STM×(number of positive tumor markers+1), was used for diagnosis. The THIs of lung, gastric, and liver cancers were 8.33, 9.63, and 5.2, respectively, while the ROC-areas under the curve for TMI were 0.862, 0.809, and 0.966. In this study, we developed a novel index for tumor heterogeneity based on the expression of various routinely evaluated serum tumor markers. Development of an evaluation system for tumor heterogeneity on the basis of this index could provide an effective diagnostic tool for some cancers. Copyright © 2015 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved.

Fault Diagnosis of a Reconfigurable Crawling–Rolling Robot Based on Support Vector Machines

Directory of Open Access Journals (Sweden)

Karthikeyan Elangovan

2017-10-01

Full Text Available As robots begin to perform jobs autonomously, with minimal or no human intervention, a new challenge arises: robots also need to autonomously detect errors and recover from faults. In this paper, we present a Support Vector Machine (SVM-based fault diagnosis system for a bio-inspired reconfigurable robot named Scorpio. The diagnosis system needs to detect and classify faults while Scorpio uses its crawling and rolling locomotion modes. Specifically, we classify between faulty and non-faulty conditions by analyzing onboard Inertial Measurement Unit (IMU sensor data. The data capture nine different locomotion gaits, which include rolling and crawling modes, at three different speeds. Statistical methods are applied to extract features and to reduce the dimensionality of original IMU sensor data features. These statistical features were given as inputs for training and testing. Additionally, the c-Support Vector Classification (c-SVC and nu-SVC models of SVM, and their fault classification accuracies, were compared. The results show that the proposed SVM approach can be used to autonomously diagnose locomotion gait faults while the reconfigurable robot is in operation.