• Nuclear Engineering and Technology
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• 제50권3호
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• pp.396-410
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• 2018

To assist operators to properly assess the current situation of the plant, accurate fault diagnosis methodology should be available and used. A reliable fault diagnosis method is beneficial for the safety of nuclear power plants. The major idea proposed in this work is integrating the merits of different fault diagnosis methodologies to offset their obvious disadvantages and enhance the accuracy and credibility of on-line fault diagnosis. This methodology uses the principle component analysis-based model and multi-flow model to diagnose fault type. To ensure the accuracy of results from the multi-flow model, a mechanical simulation model is implemented to do the quantitative calculation. More significantly, mechanism simulation is implemented to provide training data with fault signatures. Furthermore, one of the distance formulas in similarity measurement-Mahalanobis distance-is applied for on-line failure degree evaluation. The performance of this methodology was evaluated by applying it to the reactor coolant system of a pressurized water reactor. The results of simulation analysis show the effectiveness and accuracy of this methodology, leading to better confidence of it being integrated as a part of the computerized operator support system to assist operators in decision-making.

• 한국수소및신에너지학회논문집
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• 제34권5호
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• pp.478-489
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• 2023

In this study, fault diagnosis and detection methods developed to ensure the reliability of polymer electrolyte membrane (PEM) hydrogen electrolysis systems have been proposed. The proposed method consists of model development and data generation of the PEM hydrogen electrolysis system, and data-driven fault diagnosis learning model development. The developed fault diagnosis learning model describes how to detect and classify faults in the sensors and components of the system.

• Nuclear Engineering and Technology
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• 제46권6호
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• pp.753-772
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• 2014

The paper presents functional modelling and its application for diagnosis in nuclear power plants. Functional modelling is defined and its relevance for coping with the complexity of diagnosis in large scale systems like nuclear plants is explained. The diagnosis task is analyzed and it is demonstrated that the levels of abstraction in models for diagnosis must reflect plant knowledge about goals and functions which is represented in functional modelling. Multilevel flow modelling (MFM), which is a method for functional modelling, is introduced briefly and illustrated with a cooling system example. The use of MFM for reasoning about causes and consequences is explained in detail and demonstrated using the reasoning tool, the MFMSuite. MFM applications in nuclear power systems are described by two examples: a PWR; and an FBR reactor. The PWR example show how MFM can be used to model and reason about operating modes. The FBR example illustrates how the modelling development effort can be managed by proper strategies including decomposition and reuse.

• Journal of Information Processing Systems
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• 제18권6호
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• pp.822-829
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• 2022

According to existing study into the remote fault diagnosis procedure, the current diagnostic approach has an imperfect decision model, which only supports communication in a close distance. An Internet of Things (IoT)-based remote fault diagnostic approach for wind power equipment is created to address this issue and expand the communication distance of fault diagnosis. Specifically, a decision model for active power coordination is built with the mechanical energy storage of power generation equipment with a remote diagnosis mode set by decision tree algorithms. These models help calculate the failure frequency of bearings in power generation equipment, summarize the characteristics of failure types and detect the operation status of wind power equipment through IoT. In addition, they can also generate the point inspection data and evaluate the equipment status. The findings demonstrate that the average communication distances of the designed remote diagnosis method and the other two remote diagnosis methods are 587.46 m, 435.61 m, and 454.32 m, respectively, indicating its application value.

• 대한한의진단학회지
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• 제14권2호
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• pp.51-66
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• 2010

Objectives: In this study, diagnosis of an ontology-based electronic chart system works by presenting a model electronic chart system is contributing to the standardization and objectification in Oriental Medicine field. Methods: The clinic is currently used in the electronic chart, and use surveys and research utilization was diagnosed. In addition, the symptoms with medicines, prescriptions, patterns ontology data, information, relationships between the association was derived. electronic chart the flow of information from the input data stream was defined using the ontology. Medicines, prescriptions, patterns diagnosis ontology, using the process model presented in the electronic chart. Results: This study show that interoperable model within the diagnostic capabilities of the electronic chart system in Oriental Medicine and represent diagnosis process in the system with symptoms. Conclusions: Diagnosed with symptoms of ontology integration with electronic chart to study the model was placed goal. Diagnosis and prescription due to strong associative connection implies an ontology can be seen even more important. Diagnostic elements will be added to enhance the diagnostic capabilities in the electronic chart can be varied and objective diagnostic model can be presented. This study extends the range for the CDSS, and new areas of research can be presented.

• 산업경영시스템학회지
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• 제36권3호
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• pp.109-117
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• 2013

This study designed a "Diagnostic Model for Management Innovation of Medium Enterprises" based on the theoretical background of success factor and management diagnosis model for management innovation of medium enterprises and suggested a measure for utilization of strategic subject and diagnostic model that enterprises can apply. Utilization of medium enterprises management innovation diagnostic model designed through this study would be of help for making a diagnosis of the capability maturity level of enterprises' current management system and improving it by establishing a challenging capability objective and building a circulation system capable of innovating enterprises. It is expected for enterprises to overcome growing pains and establish a management system capable of achieving outcome (productivity) by repeating measurement and innovation through management diagnosis. In addition, this study provides a method to produce a strategic subject, select priority of implementation and prepare an implementation road map by classifying and filtering management issues produced as a result of management diagnosis in a systematic way. If variables necessary for production of an objective weighted value of scoring and discover of elements for category of diagnostic model and elementary items as well as design of a self-diagnosis questionnaire, measurement of management outcome suggested in this study can be able to be verified and supplemented through case study in the future, it is expected to make the degree of completion as a diagnostic model elevated that may help for growth and development through innovation of medium enterprises.

• 한국정밀공학회지
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• 제31권11호
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• pp.1031-1040
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• 2014

In this paper, a new condition diagnosis algorithm for the lens injection molding process using various features extracted from cavity pressure, nozzle pressure and screw position signals is developed with the aid of probability neural network (PNN) method. A new feature extraction method is developed for identifying five (5), seven (7) and two (2) critical features from cavity pressure, nozzle pressure and screw position signals, respectively. The node energies extracted from cavity and nozzle pressure signals are also considered based on wavelet packet decomposition (WPD). The PNN method is introduced to build the condition diagnosis model by considering the extracted features and node energies. A series of the lens injection molding experiments are conducted to validate the model, and it is demonstrated that the proposed condition diagnosis model is useful with high diagnosis accuracy.

• 설비공학논문집
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• 제18권10호
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• pp.791-799
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• 2006

The fault diagnosis technologies may be applied in order to decrease the energy consumption and the maintenance cost of an air-conditioning system. In this paper, a fault diagnosis algorithm was developed by using a neural no-fault model and a dual fuzzy logic. Five different faults, such as the compressor valve leakage, the liquid line blockage, the condenser fouling, the evaporator fouling, and the refrigerant leakage of an air-conditioning system, were considered. The fault diagnosis algorithm was tested by using a fault simulation facility. Test results showed that the algorithm developed for this study was effective to detect and diagnose various faults. Therefore, this algorithm may be practically used for the fault diagnosis of an air-conditioning system.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.1782-1787
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• 1997

A combined algorithm called APEXS(Alarm Processing and Diagnosis Expert System) for power plants has been developed on the time-based alarm processing with a proper alarm prioritization and a diagnosis with a qualitative model(QM), qualitative interpreter(QI), and a state-transition trees(STT).

• Journal of Electrical Engineering and Technology
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• 제12권2호
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• pp.830-839
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• 2017

Support vector machine (SVM) is introduced as an effective fault diagnosis technique based on dissolved gases analysis (DGA) for oil-immersed transformers with maximum generalization ability; however, the applicability of the SVM is highly affected due to the difficulty of selecting the SVM parameters appropriately. Therefore, a novel approach combing SVM with improved imperialist competitive algorithm (IICA) for fault diagnosis of oil-immersed transformers was proposed in the paper. The improved ICA, which is proved to be an effective optimization approach, is employed to optimize the parameters of SVM. Cross validation and normalizations were applied in the training processes of SVM and the trained SVM model with the optimized parameters was established for fault diagnosis of oil-immersed transformers. Three classification benchmark sets were studied based on particle swarm optimization SVM (PSOSVM) and IICASVM with four multiple classification schemes to select the best scheme for transformer fault diagnosis. The results show that the proposed model can obtain higher diagnosis accuracy than other methods. The comparisons confirm that the proposed model is an effective approach for classification problems.