• 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.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2001년도 하계종합학술대회 논문집(5)
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• pp.115-118
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• 2001

In this paper we propose an FDI(fault detection and isolation) algorithm using neural network-based multi-fault models to detect and isolate single faults in nonlinear systems. When a change in the system occurs, the errors between the system output and the neural network nominal system output cross a threshold, and once a fault in the system is detected, the fault classifier statistically isolates the fault by using the error between each neural network-based fault model output and the system output.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1239-1243
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• 2005

A multipath mitigation method using the fault detection and isolation technique is proposed for the CDGPS. The base station is assumed to be immune to the effect of the multipath. With this reasonable assumption, the effect of multipath in moving station is mitigated. For that, the double difference measurement is produced, and then another additional difference between code pseudorange and acclumulated carrier phase is calculated. The test statistic is constituted with those differences. The hypothesis testing is applied to that test statistic. The proposed test statistic makes use of the effect of multipath in code pseudoranges and it does not use time differences. Therefore the detection ability for multipath is improved in most environments. However, the increased number of differences makes the measurement noises larger. The performance of the method is compared with that of the conventional parity space method with code pseudorange.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.127.1-127
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• 2001

In this paper we propose an input/output model based fault diagnosis method to detect and isolate single faults in the robot arm control system. The proposed algorithm is functionally composed of three main parts-parameter estimation, fault detection, and isolation, When a change in the system occurs, the errors between the system output and the estimated output cross a predetermined threshold, and once a fault in the system is detected, and in this zone the estimated parameters are transferred to the fault classifier by ART2(adaptive resonance theory 2) neural network for fault isolation. Since ART2 neural network is an unsupervised neural network fault classifier does not require the knowledge of all possible faults to isolate the faults occurred in the system. Simulations are carried out to evaluate the performance of the proposed ...

• Nuclear Engineering and Technology
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• 제51권1호
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• pp.146-154
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• 2019

An improved principal component analysis (PCA) method is applied for sensor fault detection and isolation (FDI) in a nuclear power plant (NPP) in this paper. Data pre-processing and false alarm reducing methods are combined with general PCA method to improve the model performance in practice. In data pre-processing, singular points and random fluctuations in the original data are eliminated with various techniques respectively. In fault detecting, a statistics-based method is proposed to reduce the false alarms of $T^2$ and Q statistics. Finally, the effects of the proposed data pre-processing and false alarm reducing techniques are evaluated with sensor measurements from a real NPP. They are proved to be greatly beneficial to the improvement on the reliability and stability of PCA model. Meanwhile various sensor faults are imposed to normal measurements to test the FDI ability of the PCA model. Simulation results show that the proposed PCA model presents favorable performance on the FDI of sensors no matter with major or small failures.

• 전기학회논문지
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• 제57권7호
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• pp.1247-1253
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• 2008

A design method of a T-S fuzzy model based reduced order nonlinear unknown input observer(NUIO) is presented. The fuzzy NUIO is designed based on the parallel distributed compensation(PDC) concept. It consists of a number of the linear UIOs, each of which is designed for each local linear model in the T-S fuzzy model of a class of nonlinear systems. The fuzzy NUIO provides not only the state estimates insensitive to the unknown inputs, for example, disturbances and faults etc., but also the estimates of the unknown inputs. Therefore, It can be employed in the state feedback control and disturbance rejection control of a class of nonlinear systems with unknown disturbances. It also applied to the robust residual generation for the fault detection and isolation systems and to the design of fault tolerant control systems. As an example, the NUIO is applied to an inverted pendulum system to show the state and disturbance estimation performance and to illustrate the fuzzy reduced order NUIO design method.

• 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.

• 제어로봇시스템학회논문지
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• 제18권7호
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• pp.669-672
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• 2012

This paper investigates an algorithm for robust fault diagnosis in robot manipulators. The TOSM (Third Order Sliding Mode observer) provides both theoretically exact observation and unknown fault identification without filtration. The EOI (Equivalent Output Injections) of the TOSM observers can be used as residuals for the problem of fault diagnosis and to identify the unknown faults. The obtained fault information can be used for fault detection, isolation as well as fault accommodation to the self-correcting failure system. The computer simulation results for a PUMA 560 robot are shown to verify the effectiveness of the proposed strategy.

• 제어로봇시스템학회논문지
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• 제8권11호
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• pp.922-927
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• 2002

A variable structure control (VSC) based model following control system that possesses fault detection and isolation (FDI) capability as well as fault tolerance property is proposed. The nonlinear part of the proposed control law. whose magnitude is determined by sliding variables, plays the role of suppressing fault effect. Thus, approximate fault reconstruction is also possible via the analysis of sliding variables. The proposed algorithm is applied to an active suspension system of pound vehicles to verify its applicability.

• 전력전자학회논문지
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• 제12권1호
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• pp.74-80
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• 2007

본 논문에서는 2상 여자 방식으로 구동하는 전압형 인버터 스위치에 발생한 개방 고장을 검출하는 기법을 제안한다. 제안된 기법은 인버터 각 상의 하단 스위치에 전압 센서를 사용하여 동작 모드에 따라 개방 고장을 판별한다. 이는 구현이 간단하고 빠른 고장 판별이 가능하며 부하의 영향을 거의 받지 않기 때문에, 시스템의 신뢰성을 향상시킨다. 또한 4-스위치 인버터 구동을 적용한 재구성을 통하여 고장 발생시에도 고장의 영향을 최소화 하면서 연속적인 운전을 가능하게 했다. 제안된 고장 검출 알고리즘의 타당성은 실험결과로 검증한다.