• Title/Summary/Keyword: Fault Model

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Synthesis of the Fault-Causality Graph Model for Fault Diagnosis in Chemical Processes Based On Role-Behavior Modeling (역할-거동 모델링에 기반한 화학공정 이상 진단을 위한 이상-인과 그래프 모델의 합성)

  • 이동언;어수영;윤인섭
    • Journal of Institute of Control, Robotics and Systems
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    • v.10 no.5
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    • pp.450-457
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    • 2004
  • In this research, the automatic synthesis of knowledge models is proposed. which are the basis of the methods using qualitative models adapted widely in fault diagnosis and hazard evaluation of chemical processes. To provide an easy and fast way to construct accurate causal model of the target process, the Role-Behavior modeling method is developed to represent the knowledge of modularized process units. In this modeling method, Fault-Behavior model and Structure-Role model present the relationship of the internal behaviors and faults in the process units and the relationship between process units respectively. Through the multiple modeling techniques, the knowledge is separated into what is independent of process and dependent on process to provide the extensibility and portability in model building, and possibility in the automatic synthesis. By taking advantage of the Role-Behavior Model, an algorithm is proposed to synthesize the plant-wide causal model, Fault-Causality Graph (FCG) from specific Fault-Behavior models of the each unit process, which are derived from generic Fault-Behavior models and Structure-Role model. To validate the proposed modeling method and algorithm, a system for building FCG model is developed on G2, an expert system development tool. Case study such as CSTR with recycle using the developed system showed that the proposed method and algorithm were remarkably effective in synthesizing the causal knowledge models for diagnosis of chemical processes.

Model-based fault detection and isolation of a linear system (선형시스템의 모델기반 고장감지와 분류)

  • 이인수;전기준
    • Journal of the Korean Institute of Telematics and Electronics S
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    • v.35S no.1
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    • pp.68-79
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    • 1998
  • In this paper, we propose a model-based FDI(fault detetion and isolation) algorithm to detect and isolate fault in a linear system. The proposed algorithm is gased on an HFC(hydrid fault classifier) which consists of an FCART2(fault classifier by ART2 neural network) and an FCFM(fault classifier by fault models) which operate in parallel to isolate faults. The proposed algorithm is functionally composed of three main parts-parameter estimation, fault detection, and isolation. When a change in the system occurs, the estimated parameters go through a transition zone in which errors between the system output and the stimated output and the estimated output cross a predetermined thrseshold, and in this zone the estimated parameters are tranferred to the FCART2 for fault isolation. On the other hand, once a fault in the system is detected, the FCFM statistically isolates the fault by using the error between ach fault model out put and the system output. From the computer simulation resutls, it is verified that the proposed model-based FDI algorithm can be performed successfully to detect and isolate faults in a position control system of a DC motor.

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High-Level Test Generation for Asynchronous Circuits Using Signal Transition Graph (신호 전이그래프를 이용한 비동기회로의 상위수준 테스트 생성)

  • 오은정;김수현;최호용;이동익
    • Proceedings of the IEEK Conference
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    • 2000.06b
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    • pp.137-140
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    • 2000
  • In this paper, we have proposed an efficient test generation method for asynchronous circuits. The test generation is based on specification level, especially on Signal Transition Graph(STG)〔1〕 which is a kind of specification method for asynchronous circuits. To conduct a high-level test generation, we have defined a high-level fault model, called single State Transition Fault(STF) model on STG and proposed a test generation algorithm for STF model. The effectiveness of the proposed fault model and its test generation algorithm is shown by experimental results on a set of benchmarks given in the form of STG. Experimental results show that the generated test for the proposed fault model achieves high fault coverage over single input stuck-at fault model with low cost. We have also proposed extended STF model with additional gate-level information to achieve higher fault coverage in cost of longer execution time.

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Fault diagnosis based on likelihood decomposition

  • Uosaki, Katsuji;Kagawa, Tetsuo
    • 제어로봇시스템학회:학술대회논문집
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    • 1992.10b
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    • pp.272-275
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    • 1992
  • A novel fault diagnosis method based on likelihood decomposition is proposed for linear stochastic systems described by autoregressive (AR) model. Assuming that at some time instant .tau. the fault of one of the following two types is occurs: innovation fault (actuator fault); and observation fault (sensor fault), the log-likelihood function is decomposed into two components based on the observations before and after .tau., respectively, Then, the type of the fault is determined by comparing the log-likelihoods corresponding two types of faults. Numerical examples demonstrate the usefulness of the proposed diagnosis method.

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Fault Tolerant Control for Nonlinear Boiler System (비선형 보일러 시스템에서의 이상허용제어)

  • Yoon, Seok-Min;Kim, Dae-Woo;Lee, Myung-Eui;Kwon, O-Kyu
    • 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.

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Model-based Fault Diagnosis Using Quantized Vibration Signals (양자화된 진동신호를 이용한 모델기반 고장진단)

  • Kim, Do-Hyun;Choi, Yeon-Sun
    • Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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    • 2005.11a
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    • pp.279-284
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    • 2005
  • Knowledge based fault diagnosis has a limitation in determining the cause and scheme for the fault, because it detects faults from signal pattern only Therefore, model-based fault diagnosis is requested to determine the fault by analyzing output of the equipment from its dynamic model. This research shows a method how to devise the automaton of system as a model for normal and faulty condition through the reduction of handling data by quantization of vibration signals and the example which is concerning to the bearing of ATM. The developed model based fault diagnosis was applied to detect the faulty bearing of ATM, which results.

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Fuzzy Model-Based Fault Detection Method of EPB System for Varying Temperature (온도변화에 강인한 EPB 시스템의 퍼지모델 기반 고장검출 방법)

  • Moon, Byoung-Joon;Kim, Dong-Han;Park, Chong-Kug
    • Journal of Institute of Control, Robotics and Systems
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    • v.15 no.10
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    • pp.1009-1013
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    • 2009
  • In this paper, a robust fault detection method for varying temperature based on fuzzy model is proposed. To develop a robust force estimation model, it needs temperature information because the output of force sensor is affected by a temperature variation. The nonlinear dynamic system, such as the parking force of the EPB (Electronic Parking Brake) system is necessary to have a higher order equation model. But, because of the calculation time, the higher order equation model is hard to be used in real application. In case of the lower order equation model, the result is not as accurate as acceptable. To solve this problem, the robust fuzzy model-based fault detection is developed. A proposed fault detection method for varying temperature is verified by HILS (hardware in the loop simulation).

The Development of a Fault Diagnosis Model based on the Parameter Estimations of Partial Least Square Models (부분최소제곱법 모델의 파라미터 추정을 이용한 화학공정의 이상진단 모델 개발)

  • Lee, Kwang Oh;Lee, Chang Jun
    • Journal of the Korean Society of Safety
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    • v.34 no.4
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    • pp.59-67
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    • 2019
  • Since it is really hard to construct process models based on prior process knowledges, various statistical approaches have been employed to build fault diagnosis models. However, the crucial drawback of these approaches is that the solutions may vary according to the fault magnitude, even if the same fault occurs. In this study, the parameter monitoring approach is suggested. When a fault occurs in a chemical process, this leads to trigger the change of a process model and the monitoring parameters of process models is able to provide the efficient fault diagnosis model. A few important variables are selected and their predictive models are constructed by partial least square (PLS) method. The Euclidean norms of parameters of PLS models are estimated and a fault diagnosis can be performed as comparing with parameters of PLS models based on normal operational conditions. To improve the monitoring performance, cumulative summation (CUSUM) control chart is employed and the changes of model parameters are recorded to identify the type of an unknown fault. To verify the efficacy of the proposed model, Tennessee Eastman (TE) process is tested and this model can be easily applied to other complex processes.

Application of robust fault detection for DC motor considering system uncertainty (불확실성을 고려한 DC Motor의 견실한 이상검출)

  • 김대우;유호준;권오규
    • 제어로봇시스템학회:학술대회논문집
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    • 1997.10a
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    • pp.856-859
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    • 1997
  • In this paper we treat the application of fault detection method in DC motor having both model mismatch and noise problems. A fault detection method presented by Kwon et al. (1994) for SISO systems has been here experimented. The model mismatch includes here linearization error as well as undermodelling. Comparisons are made with the real plant, DC motor. The experimental result of robust fault detection method is shown to have good performance via with the alternative fault detection method which do not account noise.

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A Study on Speed Improvement of Gate Delay Test Generator for Combinational Circuits (조합회로에 대한 게이트 지연 검사 패턴 생성기의 속도 향상에 관한 연구)

  • 박승용;김규철
    • 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.

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