• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.116.1-116
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• 2001

In the near future, drive-by-wire systems will replace mechanical systems of vehicles. Since there would be no mechanical redundancy in the x-by-wire subsystem, it needs to improve the reliability of the system using fault diagnosis of sensors and actuators. This paper proposes a Kalman filter based fault diagnosis method for the vehicle with the drive-by-wire system, which includes steer-by-wire, brake-by-wire and throttle-by-wire systems. We will show that the proposed method is successful in fault detection and isolation for single sensor/actuator faults of the vehicle system.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.39 no.1
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• pp.42-50
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• 2002

In this paper, we propose an FDI(fault detection and isolation) method using neural network-based multi-fault models to detect and isolate 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. From the computer simulation results, it is verified that the proposed fault diagonal method can be performed successfully to detect and isolate faults in a nonlinear system.

• 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.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.769-772
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• 1999

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

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.7
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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.

• Proceedings of the KIEE Conference
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• 2001.11c
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• pp.42-45
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• 2001

In the speed-sensorless induction motor control systems, only a few percents of error in current measurement badly deteriorates the control performance. And early detection and accomodation of the faults of current sensor is very important to enhance the reliability of the induction motor control system. In this paper, we propose two sensor fault detection schemes having desired functions; fault detection, isolation of failed sensor and compensation of fault effect. The two schemes operate in real-time and employ EKFs (Extended Kalman Filter) for residual generation. Simulation results show that the proposed schemes are very useful in maintaining the control performance of the induction motor driven servo systems even in the face of sensor faults.

• International Journal of Control, Automation, and Systems
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• v.6 no.1
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• pp.142-148
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• 2008

In this paper, we deal with the problem of reducing the order of the detection filter for the linear time-invariant system. Even if the detection filter is generally designed in the form of full order linear observer, we show that it is possible to reduce its order when the response of fault signals is limited to a subspace of the estimation state space. We propose a method to extract the subspace using the observer canonical form considering the dynamics related to the remaining subspace acts as a disturbance. We designed a reduced order detection filter to reject the disturbance as well as to guarantee fault detection and isolation. A simulation result for a 5th order system is presented as an illustrative example of the proposed design method.

• Journal of Power Electronics
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• v.16 no.3
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• pp.1097-1109
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• 2016

Fault detection and isolation are related to system monitoring, identifying when a fault has occurred, and determining the type of fault and its location. Fault detection is utilized to determine whether a problem has occurred within a certain channel or area of operation. Fault detection and diagnosis have become increasingly important for many technical processes in the development of safe and efficient advanced systems for supervision. This paper presents an integrated technique for fault diagnosis and classification for open- and short-circuit faults in three-phase inverter circuits. Discrete wavelet transform and principal component analysis are utilized to detect the discontinuity in currents caused by a fault. The features of fault diagnosis are then extracted. A fault dictionary is used to acquire details about transistor faults and the corresponding fault identification. Fault classification is performed with a fuzzy logic system and relevance vector machine (RVM). The proposed model is incorporated with a set of optimization techniques, namely, evolutionary particle swarm optimization (EPSO) and cuckoo search optimization (CSO), to improve fault detection. The combination of optimization techniques with classification techniques is analyzed. Experimental results confirm that the combination of CSO with RVM yields better results than the combinations of CSO with fuzzy logic system, EPSO with RVM, and EPSO with fuzzy logic system.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2014-2016
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• 2003

A new residual generation scheme that can be employed in the process fault detection and isolation systems for linear (control) systems is suggested. The scheme is very simple, but provides the same information for the detection and isolation of the anticipated faults as the conventional multiple observer based schemes. Application results show the practical feasibility of the proposed scheme.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.10
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• pp.983-990
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• 2008

This paper presents the fault detection and diagnosis(FDD) algorithm to enhance reliability of a longitudinal controller for an autonomous All-Terrain Vehicle(ATV). The FDD is designed to monitor and identify faults which may occur in distributed hardware used for longitudinal control, e.g., DSPs, CAN, sensors, and actuators. The proposed FDD is an integrated approach of decentralized and centralized FDD. While the former is processed in a DSP and suitable to detect faults in a single hardware, it is sensitive to noise and disturbance. On the other hand, the latter is performed via communication and it detects and diagnoses faults through analyzing concurrent performances of multiple hardware modules, but it is limited to isolate faults specifically in terms of components in the single hardware. To compensate for disadvantages of each FDD approach, two layered structure including both decentralized and centralized FDD is proposed and it allows us to make more robust fault detection and more specific fault isolation. The effectiveness of the proposed method will be validated experimentally.