• 제목/요약/키워드: Model-based fault detection

검색결과 261건 처리시간 0.028초

S-분포형 결함 발생률을 고려한 NHPP 소프트웨어 신뢰성 모형에 관한 비교 연구 (The Comparative Software Reliability Model of Fault Detection Rate Based on S-shaped Model)

  • 김희철;김경수
    • 융합보안논문지
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    • 제13권1호
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    • pp.3-10
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    • 2013
  • 본 연구에서는 소프트웨어 제품 테스팅 과정에서 관측고장시간에 근거한 결함 발생률을 고려한 소프트웨어 신뢰성 모형에 대하여 연구 하였다. 신뢰성 분야에서 많이 사용되는 S-분포모형을 이용한 새로운 결함 확률을 추가한 문제를 제시하였다. 수명분포는 유한고장 비동질적인 포아송과정을 이용하였다 본 논문의 결함 발생률을 고려한 소프트웨어 고장 자료 분석에서는 고장 시간 자료를 적용하였으며 모수추정 방법은 최우추정법을 이용하여 결함 발생 확률에 대한 관계와 신뢰도를 추정 하였다.

상태감시컴포넌트를 사용한 OPRoS 프레임워크의 고장감내 기법 (State-Monitoring Component-based Fault-tolerance Techniques for OPRoS Framework)

  • 안희준;안상철
    • 제어로봇시스템학회논문지
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    • 제16권8호
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    • pp.780-785
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    • 2010
  • The OPRoS (Open Platform for Robotic Services) framework is proposed as an application runtime environment for service robot systems. For the successful deployment of the OPRoS framework, fault tolerance support is crucial on top of its basic functionalities of lifecycle, thread and connection management. In the previous work [1] on OPRoS fault tolerance supports, we presented a framework-based fault tolerance architecture. In this paper, we extend the architecture with component-based fault tolerance techniques, which can provide more simplicity and efficiency than the pure framework-based approach. This argument is especially true for fault detection, since most faults and failure can be defined when the system cannot meet the requirement of the application functions. Specifically, the paper applies two widely-used fault detection techniques to the OPRoS framework: 'bridge component' and 'process model' component techniques for fault detection. The application details and performance of the proposed techniques are demonstrated by the same application scenario in [1]. The combination of component-based techniques with the framework-based architecture would improve the reliability of robot systems using the OPRoS framework.

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

  • 윤석민;김대우;이명의;권오규
    • 제어로봇시스템학회논문지
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    • 제6권4호
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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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RNN-based integrated system for real-time sensor fault detection and fault-informed accident diagnosis in nuclear power plant accidents

  • Jeonghun Choi;Seung Jun Lee
    • Nuclear Engineering and Technology
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    • 제55권3호
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    • pp.814-826
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    • 2023
  • Sensor faults in nuclear power plant instrumentation have the potential to spread negative effects from wrong signals that can cause an accident misdiagnosis by plant operators. To detect sensor faults and make accurate accident diagnoses, prior studies have developed a supervised learning-based sensor fault detection model and an accident diagnosis model with faulty sensor isolation. Even though the developed neural network models demonstrated satisfactory performance, their diagnosis performance should be reevaluated considering real-time connection. When operating in real-time, the diagnosis model is expected to indiscriminately accept fault data before receiving delayed fault information transferred from the previous fault detection model. The uncertainty of neural networks can also have a significant impact following the sensor fault features. In the present work, a pilot study was conducted to connect two models and observe actual outcomes from a real-time application with an integrated system. While the initial results showed an overall successful diagnosis, some issues were observed. To recover the diagnosis performance degradations, additive logics were applied to minimize the diagnosis failures that were not observed in the previous validations of the separate models. The results of a case study were then analyzed in terms of the real-time diagnosis outputs that plant operators would actually face in an emergency situation.

Fault Feature Clarification in the Residual for Fault Detection and Diagnosis of Control Systems

  • Lee, Jonghyo;Joon Lyou
    • 제어로봇시스템학회:학술대회논문집
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    • 제어로봇시스템학회 2002년도 ICCAS
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    • pp.96.3-96
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    • 2002
  • A scheme of clarifying fault feature in the residual is given for model-based fault detection and diagnosis of control systems. It is based on the residual generation using a robust filter and the noise suppresion in test statistics of the residual by multi-scale discrete wavelet transform. By clarifying the fault feature in the residual, the difficulties of existing model based approaches via adopting a threshold can be overcomed and it has advantage of taking the false alarm and missed detection into acount at the same time, which can make the fault detection and diagnosis easy and correct. To show the effectiveness of our approach, the simulation results are illustrated for a linear syste...

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미지입력을 포함한 시스템의 관측기 기반 견실고장진단 및 재구성 적응제어 (Observer-Based Robust Fault Diagnosis and Reconfigurable Adaptive Control for Systems with Unknown Inputs)

  • 최재원;이승우;서영수
    • 제어로봇시스템학회논문지
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    • 제8권11호
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    • pp.928-934
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    • 2002
  • A natural way to cope with fault tolerant control (FTC) problems is to modify the control parameters according to an online identification of the system parameters when a fault occurs. However. due to not only difficulties Inherent to the online multivariable identification in closed-loop systems, such as modeling errors, noise or the lack of excitation signals, but also long time requirement to identify the post-fault system and implemeutation of control problems during the identification process, we propose an alternative approach based on the observer-based fault detection and isolation (FDI) and model reference adaptive control (MRAC). The proposed robust fault diagnosis method is based on a bank of observers. We also propose a model reference adaptive control with changeable reference models according to the occurred faults. Simulation results of a flight control example show the validity and applicability of the proposed algorithms.

IMS용 로봇에서의 FDI기법 연구 (Fault detection and identification for a robot used in intelligent manufacturing)

  • 이상길;송택렬
    • 제어로봇시스템학회:학술대회논문집
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    • 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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    • pp.1489-1492
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    • 1997
  • To increase reliability and performance of an IMS(Intelligent Manufacturing System), fault tolerant control based on an accurate fault diagnosis is needed. In this paper, robot FDI(fault detection and identification) is proposed for IMS where the robot is controlled with state estimates of a nonlinear filter using a mathematical robot model. The Chi-square distribution is applied fault detection and fault size is estimated by a proposed bias filter. Performance of the proposed algorithm is tested by simulation for studies.

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IMS용 로봇의 고장진단기법에 관한 연구 (Fault Detection and Identification for a Robot used in Intelligent Manufacturing)

  • 이상길;송택렬
    • 제어로봇시스템학회논문지
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    • 제4권5호
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    • pp.666-673
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    • 1998
  • To increase reliability and performance of an IMS(Intelligent Manufacturing System), fault tolerant control based on an accurate fault diagnosis is needed. In this paper, robot FDI(fault detection and identification) is proposed for IMS where the robot is controlled with state estimates of a nonlinear filter using a mathematical robot model. The Chi-square test and GLR(General likelihood ratio) test are applied for fault detection and fault size is estimated by a proposed bias filter. Performance of the proposed algorithm is tested by simulation for studies.

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A Study on Fault Detection of a Turboshaft Engine Using Neural Network Method

  • Kong, Chang-Duk;Ki, Ja-Young;Lee, Chang-Ho
    • International Journal of Aeronautical and Space Sciences
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    • 제9권1호
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    • pp.100-110
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    • 2008
  • It is not easy to monitor and identify all engine faults and conditions using conventional fault detection approaches like the GPA (Gas Path Analysis) method due to the nature and complexity of the faults. This study therefore focuses on a model based diagnostic method using Neural Network algorithms proposed for fault detection on a turbo shaft engine (PW 206C) selected as the power plant for a tilt rotor type unmanned aerial vehicle (Smart UAV). The model based diagnosis should be performed by a precise performance model. However component maps for the performance model were not provided by the engine manufacturer. Therefore they were generated by a new component map generation method, namely hybrid method using system identification and genetic algorithms that identifies inversely component characteristics from limited performance deck data provided by the engine manufacturer. Performance simulations at different operating conditions were performed on the PW206C turbo shaft engine using SIMULINK. In order to train the proposed BPNN (Back Propagation Neural Network), performance data sets obtained from performance analysis results using various implanted component degradations were used. The trained NN system could reasonably detect the faulted components including the fault pattern and quantity of the study engine at various operating conditions.

Motion Sensor Fault Detection and Failsafe Logic for Vehic1e Stability Control Systems (VSCs)

  • Yi, Kyongsu;Min, Kyongchan
    • Journal of Mechanical Science and Technology
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    • 제18권11호
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    • pp.1961-1968
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    • 2004
  • The design of a reliable and failsafe control system requires that sensor failures be detected and identified within acceptable time limit so that system malfunction can be prevented. This paper presents a model-based approach to sensor fault detection with applications to vehicle stability control systems. The effectiveness of the proposed method is illustrated through test data-based evaluation. Vehicle test data-based evaluation results show that the proposed fault management scheme can be used for the design of a failsafe VSCs.