• Transactions on Control, Automation and Systems Engineering
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• v.1 no.1
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• pp.54-61
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• 1999

FCM(Fuzzy Cognitive Map) is proposed for representing causal reasoning. Its structure allows systematic causal reasoning through a forward inference. By using the FCM, authors have proposed FCM-based fault diagnostic algorithm. However, it can offer multiple interpretations for a single fault. In process engineering, as experience accumulated, some form of quantitative process knowledge is available. If this information can be integrated into the FCM-based fault diagnosis, the diagnostic resolution can be further improved. The purpose of this paper is to propose an enhanced FCM-based fault diagnostic scheme. Firstly, the membership function of fuzzy set theory is used to integrate quantitative knowledge into the FCM-based diagnostic scheme. Secondly, modified TAM recall procedure is proposed. Considering that the integration of quantitative knowledge into FCM-based diagnosis requires a great deal of engineering efforts, thirdly, an automated procedure for fusing the quantitative knowledge into FCM-based diagnosis is proposed by utilizing self-learning feature of neural network. Finally, the proposed diagnostic scheme has been tested by simulation on the two-tank system.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.10
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• pp.575-582
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• 2003

A new FDOs (fault diagnostic observers) and the residual generation schemes using the FDOs are suggested for the process fault detection and isolation of linear (control) systems. The design method of the FDO is described, first, for the full measurement systems. Then it is extended for the systems with unmeasurable state variables. An unknown input observer is proposed and applied for the extension. The size of the observer bank may be the smallest, specially in full measurement systems, because the order of the proposed FDO is very low. In spite of the simplicity, the scheme provides the same information for the detection and isolation of the anticipated faults as the conventional multiple observer based schemes. The residuals may be structured so that fault isolation can be performed by pre-selected logic. An FDIS using the proposed scheme is constructed for the model of the four-tank system. Simulation results show the practical feasibility of the proposed scheme.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.04b
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• pp.457-462
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• 1995

This paper describes diagnosis strategy of the Flexible Multiple Fault Diagnosis Module for forecasting faults in system and deciding current machine state form sensor information. Most studydeal with diagnosis control stategy about single fault in a system, this studies deal with multiple fault diagnosis. This strategy is consist of diagnosis control module such as backward tracking expert system shell, various neural network, numerical model to predict machine state and communication module for information exchange and cooperate between each model. This models are used to describe structure, function and behavior of subsystem, complex component and total system. Hierarchical structure is very efficient to represent structural, functional and behavioral knowledge. FT(Fault Tree). ST(Symptom Tree), FCD(Fault Consequence Diagrapy), SGM(State Graph Model) and FFM(Functional Flow Model) are used to represent hierachical structure. In this study, IA(Intelligent Agent) concept is introduced to match FT component and event symbol in diagnosed system and to transfer message between each event process. Proposed diagnosis control module is made of IPC(Inter Process Communication) method under UNIX operating system.

• Journal of the Korean Society of Safety
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• v.29 no.4
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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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• v.39 no.1
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• pp.51-62
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• 2007

To efficiently design safety-critical systems such as nuclear power plants, with the requirement of high reliability, methodologies allowing for rigorous interactions between the synthesis and analysis processes have been proposed. This paper attempts to develop a reliability-centered design framework through an interactive process between Axiomatic Design (AD) and Fault Tree Analysis (FTA). Integrating AD and FTA into a single framework appears to be a viable solution, as they compliment each other with their unique advantages. AD provides a systematic synthesis tool while FTA is commonly used as a safety analysis tool. These methodologies build a design process that is less subjective, and they enable designers to develop insights that lead to solutions with improved reliability. Due to the nature of the two methodologies, the information involved in each process is complementary: a success tree versus a fault tree. Thus, at each step a system using AD is synthesized, and its reliability is then quantified using the FT derived from the AD synthesis process. The converted FT provides an opportunity to examine the completeness of the outcome from the synthesis process. This study presents an example of the design of a Containment Heat Removal System (CHRS). A case study illustrates the process of designing the CHRS with an interactive design framework focusing on the conversion of the AD process to FTA.

• 제어로봇시스템학회:학술대회논문집
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• 1987.10b
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• pp.426-430
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• 1987

The reliability is the critical issue in many computer applications, particularly in process control system. In this paper we describe how to achieve the reliability improvement in controller system based multiprocessor. The proposed method is accomplished by using the techniques of fault detection, fault isolation, safe action, and fault diagnosis.

• Proceedings of the IEEK Conference
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• 1998.10a
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• pp.723-726
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• 1998

Fault dropping is a very important part of test generation process. It is used to reduce test generation time. Test generation systems use fault simulation for the purpose of fault dropping by identifying detectable faults with generated test patterns. Two kinds of delay fault model is used in practice, path delay fault model and gate delay fault model. In this paper we propose an efficient method for gate delay test generation which shares second test vector.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.339-343
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• 1990

This paper describes a neural-network-based methodology for providing a potential solution in the area of process fault diagnosis. The existing neural network for fault diagnosis learn fault node by using pairs of single-symptom-single-cause only. But in real plants, the effect of a fault propagates continuously from it's origin; different sensor values reflect this. In this paper, we suggest a new method which can handle the effect of symptom propagation. The proposed method can find the exact origin of the fault of which the symptom is propagated continuously with time.

• Journal of the Korean Society of Propulsion Engineers
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• v.20 no.2
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• pp.56-66
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• 2016

Robustness is essential for model based FDI (Fault Detection and Isolation) and it is inevitable to have modeling errors and sensor signal noises during the process of FDI. This study suggests an improved method by applying NARX (Nonlinear Auto Regressive eXogenous) model and Kalman estimator in order to cope with problems caused by linear model errors and sensor signal noises in the process of fault diagnoses. Fault decision is made by the probability of the trend of gradually accumulated errors applying Fuzzy logic, which are robust to instantaneous sensor signal noises. Reliability of fault diagnosis is verified under various fault simulations.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.4
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• pp.254-260
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• 2000

This paper deals with the development of fault tolerant control for a nonlinear boiler system with noise and disturbance. The MCMBPC(Multivariable Constrained Model Based Predictive Control) is adopted for the control of the specific boiler turbin model. The fault detection and diagnosis are accomplished with the Kalman filter and two bias estimators. Once a fault is detected, two Bias estimators are driven to estimate the fault and to discriminate Process fault and sensor fault. In this paper, a fault tolerant control scheme combining MCMBPC with a fault compensation method based on the bias estimator is proposed. The proposed scheme has been applied to the nonlinear boiler system and shown a satisfactory performance through some simulations.