• Proceedings of the Korea Contents Association Conference
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• 2003.11a
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• pp.276-280
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• 2003

Somani and Peleg proposed t/k-diagnosable system to diagonse more faults than t(dimension) by allowing upper bounded few number of units to be diagnosed incorrectly. Kranakis and Pelc showed that their adaptive diagnosis algorithm was more efficient than that of any previous ones, assuming that the number of faults does not exceed the hypercube dimension. We propose an adaptive diagnosis algorithm using the idea of t/k-diagnosable system on the basis of that of Kranakis and Pelc's. When the number of faults exceeds t, we allow a fault(k=1, 2, 3) to be diagnosed incorrectly. Based on this idea, we find that the performance of the proposed algorithm is nearly as efficient as any previously known strategies and detect above about double faults.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.9
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• pp.822-826
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• 2013

In this paper, we propose an AFDO (Adaptive Fault Diagnosis Observer) and a fault tolerant controller for a class of nonlinear continuous-time system under the nonlinear abrupt actuator faults. Together with its estimation laws, the AFDO which estimates that the actuator faults is designed by using the Lyapunov analysis. Then, based on the designed AFDO, an adaptive sliding mode controller is proposed as the fault tolerant controller. Using Lyapunov stability analysis, we also prove the uniform boundedness of the state, the output and the fault estimation errors, and the asymptotic stability of the tracking error under the nonlinear time-varying faults. Finally, we illustrate the effectiveness of the proposed diagnosis method and the control scheme thorough computer simulations.

• Transactions of the Korean Society of Automotive Engineers
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• v.15 no.6
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• pp.9-16
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• 2007

This paper describes the design and implementation of the fault diagnosis on the Electronic Throttle Control(ETC) System. The proposed fault diagnosis consists of an input signal, actuator and a processor diagnosis. The input signal diagnosis can detect the faults of the ETC system's input signals such as the position sensor fault, source voltage fault, load current fault, and desired position fault. The actuator diagnosis is able to detect the actuator fault due to the actuator aging and an obstacle which interfere in the movement of the actuator. The processor diagnosis detects the fault which prevents the microprocessor from operating the ETC software. In order to protect the breakdown of the ETC system and assure the driving safety, appropriate reactions are also proposed according to the detected faults. The safety and reliability of the ETC system can be improved by the proposed fault diagnosis.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.43 no.9 s.351
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• pp.59-63
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• 2006

With the increasing complexity of VLSI devices, more complex faults have appeared. Many methods for diagnosing the single stuck-at fault have been studied. Often multiple defects on a foiling chip better reflect the reality. So, we propose an efficient diagnosis algorithm for multiple stuck-at faults. By using vectorwise intersections as an important metric of diagnosis, the proposed algorithm can diagnose multiple defects using single stuck-at fault simulator. In spite of multiple fault diagnosis, the number of candidate faults is also drastically reduced. For fault identification, positive calculations and negative calculations based on variable weights are used for the matching algorithm. Experimental results for ISCAS85 and full-scan version of ISCAS89 benchmark circuits prove the efficiency of the proposed algorithm.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.1
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• pp.241-248
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• 2011

In this paper we proposed and implemented a diagnosis system to control nuclear fuel handling machine. The proposed system consists of data acquisition system, diagnosis algorithm and faults simulator. Since the test on real operation of the fuel handling machine is impossible, we evaluated the proposed system by diagnosis experiments using the faults simulator, with which test signals on abnormal states of the bearing ball and the inner race of the bearing are generated. The experiments showed that resulting diagnosis analysis are consistent with the theoretical expectations.

• Journal of Electrical Engineering and Technology
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• v.8 no.2
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• pp.288-294
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• 2013

The diagnosis of motor failures using an on-line method has been the aim of many researchers and studies. Several spectral analysis techniques have been developed and are used to facilitate on-line diagnosis methods in industry. This paper discusses the first application of a motor flux spectral analysis to the identification of broken rotor bar (BRB) faults in induction motors using a multiple signal classification (MUSIC) technique as an on-line diagnosis method. The proposed method measures the leakage flux in the radial direction using a radial flux sensor which is designed as a search coil and is installed between stator slots. The MUSIC technique, which requires fewer number of data samples and has a higher detection accuracy than the traditional fast Fourier transform (FFT) method, then calculates the motor load condition and extracts any abnormal signals related to motor failures in order to identify BRB faults. Experimental results clearly demonstrate that the proposed method is a promising candidate for an on-line diagnosis method to detect motor failures.

• International Journal of Automotive Technology
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• v.6 no.4
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• pp.341-350
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• 2005

In this study, fault diagnosis and monitoring of serial wound pre-engaged starter motors have been carried out. Starter motors are DC motors that enable internal combustion engine (ICE) to run. In case of breakdown of a starter motor, internal combustion engine can not be worked. Starter motors have vital importance on internal combustion engines. Kohonen network based fault diagnosis system is proposed for fault diagnosis and monitoring of starter motors. A graphical user interface (GUI) software has been developed by using Visual Basic 6.0 for fault diagnosis. Six faults, seen in starter motors, have been diagnosed successfully by using the developed fault diagnosis system. GUI software makes it possible to diagnose the faults in starter motors before they occur by keeping fault records of past occurrences.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10b
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• pp.109-114
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• 1993

It is well known that neural networks can be used to diagnose multiple faults to some limited extent. In this work we present a Multiple Fault Diagnosis Method (MFDM) via neural network which can effectively diagnose multiple faults. To diagnose multiple fault, the proposed method finds the maximum value in the output nodes of the neural network and decreases the node value by changing the hidden node values. This method can find the other faults by computing again with the changed hidden node values. The effectiveness of this method is explored through a neural-network-based fault diagnosis case study of a fluidized catalytic cracking unit (FCCU).

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.11a
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• pp.895-902
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• 2007

Rotating machinery has two typical faults with clearance, one is partial rub and the other is looseness. Due to these faults, non-linear and non-stationary signals are occurred. Therefore, time-frequency analysis is necessary for exact fault diagnosis of rotating machinery. In this paper newly developed time-frequency analysis method, HHT(Hilbert-Huang Transform) is applied to fault diagnosis and compared with other method of FFT, SFFT and CWT. The results show that HHT can represent better resolution than any other method. Consequently, the faults of rotating machinery are diagnosed efficiently by using HHT.

• Journal of Electrical Engineering and Technology
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• v.10 no.4
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• pp.1558-1565
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• 2015

In this paper, we propose a new method for detecting bearing faults using vibration signals. The proposed method is based on support vector machines (SVMs), which treat the harmonics of fault-related frequencies from vibration signals as fault indices. Using SVMs, the cross-validations are used for a training process, and a two-stage classification process is used for detecting bearing faults and their status. The proposed approach is applied to outer-race bearing fault detection in three-phase squirrel-cage induction motors. The experimental results show that the proposed method can effectively identify the bearing faults and their status, hence improving the accuracy of fault diagnosis.