• 한국안전학회지
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• 제29권4호
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• pp.73-77
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• 2014

In the field of fault diagnosis, the deviations from normal operating conditions are monitored to identify the type of faults and find their root causes. One of the most representative methods is the statistical approaches, due to a large amount of advantages. However, ambiguous diagnosis results can be generated according to fault magnitudes, even if the same fault occurs. To tackle this issue, this work proposes principal component analysis (PCA) based method with qualitative information. The PCA model is constructed under normal operation data and the residuals from faulty conditions are calculated. The significant changes of these residuals are recorded to make the information for identifying the types of fault. This model can be employed easily and the tasks for building are smaller than these of other common approaches. The efficacy of the proposed model is illustrated in Tennessee Eastman process.

• Nuclear Engineering and Technology
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• 제55권6호
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• pp.2096-2106
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• 2023

Rotating machinery is widely applied in important equipment of nuclear power plants (NPPs), such as pumps and valves. The research on intelligent fault diagnosis of rotating machinery is crucial to ensure the safe operation of related equipment in NPPs. However, in practical applications, data-driven fault diagnosis faces the problem of small and imbalanced samples, resulting in low model training efficiency and poor generalization performance. Therefore, a deep convolutional conditional generative adversarial network (DCCGAN) is constructed to mitigate the impact of imbalanced samples on fault diagnosis. First, a conditional generative adversarial model is designed based on convolutional neural networks to effectively augment imbalanced samples. The original sample features can be effectively extracted by the model based on conditional generative adversarial strategy and appropriate number of filters. In addition, high-quality generated samples are ensured through the visualization of model training process and samples features. Then, a deep convolutional neural network (DCNN) is designed to extract features of mixed samples and implement intelligent fault diagnosis. Finally, based on multi-fault experimental data of motor and bearing, the performance of DCCGAN model for data augmentation and intelligent fault diagnosis is verified. The proposed method effectively alleviates the problem of imbalanced samples, and shows its application value in intelligent fault diagnosis of actual NPPs.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2001년도 춘계학술대회 논문집
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• pp.92-97
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• 2001

The major faults of CNC machine tool is operational error which is charge over 70%. This paper describes model of remote service and fault diagnosis for CNC machine tool with open architecture controller. For intelligent fault diagnosis, new model is proposed. In this paper, the three major operational faults, emergency stop error, cycle start disable and machine ready disable, are defined. Two diagnostic models based on the ladder diagram, switching function model, step switching function model, are proposed. For internet based remote service, suitable environment is proposed and implemented with web server and client.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1997년도 추계학술대회 논문집
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• pp.442-445
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• 1997

A challenging activity in the manufacturing industry is to perform in real time the continuous monitoring of the process state, the situation assessment and identification of the problem on line and diagnosis of the cause and importance of the problem if he process does not work properly. This paper describes LAT(Ladder Analysis Tool) system for fault tree generation to improving the fault diagnosis of CNC machine tools. The system consists of 4 steps which can automatically ladder analysis from ladder diagram to two diagnosis function models. The two diagnostic models based on he ladder diagram is switching function model and step switching function model. This system tries to overcome diagnosis deficiencies present machine tool.

• 동력기계공학회지
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• 제9권4호
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• pp.187-193
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• 2005

In this paper, we proposes a man-machine interface design for fault diagnosis system with inter-node search method in a Petri net model. First, complicated fault cases are modeled as the Petri net graph expressions. Next, to find out causes of the faults on which we focus, a Petri net model is analyzed using the backward reasoning of transition-invariance in the Petri net. In this step, the inter-node search method algorithm is applied to the Petri net model for reducing the range of sources in faults. Finally, the proposed method is applied to a fault diagnosis of a refrigeration system to confirm the validity of the proposed method.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 Proceedings of the Korea Automatic Control Conference, 11th (KACC); Pohang, Korea; 24-26 Oct. 1996
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• pp.283-286
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• 1996

This study suggests a new methodology for the fault diagnosis based on the signed digraph in developing the fault diagnosis system of a boiler plant. The suggested methodology uses the new model, fault-effect tree. The SDG has the advantage, which is simple and graphical to represent the causal relationship between process variables, and therefore is easy to understand. However, it cannot handle the broken path cases arisen from data uncertainty as it assumes consistent path. The FET is based on the SDG to utilize the advantages of the SDG, and also covers the above problem. The proposed FET model is constructed by clustering of measured variables, decomposing knowledge base and searching the fault propagation path from the possible faults. The search is performed automatically. The fault diagnosis system for a boiler plant, ENDS was constructed using the expert system shell G2 and the advantages of the presented method were confirmed through case studies.

• Journal of international Conference on Electrical Machines and Systems
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• 제2권1호
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• pp.23-30
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• 2013

This paper proposes a parametric model for inter-turn fault detection in a fault-tolerant permanent-magnet (FTPM) machine, which can predict the effect of the short-circuit fault to various physical quantity of the machine. For different faulty operations, a new effective stator inter-turn fault detection method is proposed. Finally, simulations of vector-controlled FTPM machine drives are given to verify the feasibility of the proposed method, showing that even single-coil short-circuit fault could be exactly detected.

• 한국콘텐츠학회논문지
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• 제11권10호
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• pp.49-58
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• 2011

결함 트리 분석(Fault Tree Analysis)은 결함 트리를 구축하여 시스템의 안전성 분석을 수행한다. 그러나 결함 트리를 구성하는 작업은 대상 시스템의 도메인에 대한 지식과 경험을 필요로 하며 많은 시간과 노력을 소요한다. 이 논문에서는 시스템 설계 산출물인 상태 전이 모델을 기반으로 결함 트리를 체계적으로 구성하는 방법을 제안한다. 이를 위해 시스템 상태 전이 모델의 안정성 확보에 필요한 조건들을 식별하고 결함 트리를 구성할 수 있는 템플리트를 개발한다. 이 논문에서는 제안된 방법을 철도 건널목 제어 시스템에 적용한 결과도 기술한다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2007년도 제38회 하계학술대회
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• pp.1724-1725
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• 2007

This paper presents a fault detection and isolation(FDI) scheme for a nonlinear systems with a actuator and sensor faults. A residual generator based on the observer model generate the information for a fault detection. The proposed fault estimators are activated for a fault isolation and applied to estimate the time-varying lumped faults(model uncertainty + fault). but a fault estimator error dose not converge to zero since the derivative of lumped fault is not zero. Then the fuzzy neural network(FNN) is used to estimate the fault estimator error. Simulation results are presented to illustrate the effectiveness and the applicability of the approaches proposed.