• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.974-977
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• 2003

The GPS/INS integration system navigation can provide improved navigation performance and has been widely used as a main navigation system for military and commercial vehicles. When two navigation systems are tightly coupled and the structure is complicated, a fault in either the GPS or the INS can lead to a disastrous failure of the whole integration system. This paper proposes a real-time fault detection method for an AGPS/INS integration system. The proposed fault detection method comprises a BIT and a fault detection algorithm based on chi-square test. It is implemented by real-time software modules to apply the AGPS/INS integration system and van test is carried out to evaluate its performance.

• Journal of Electrical Engineering and Technology
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• v.4 no.3
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• pp.389-395
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• 2009

Industrial motors are subject to incipient faults which, if undetected, can lead to motor failure. The necessity of incipient fault detection can be justified by safety and economical reasons. The technology of artificial neural networks has been successfully used to solve the motor incipient fault detection problem. This paper develops inexpensive, reliable, and noninvasive NN based incipient fault detection scheme for small and medium sized induction motors. Detailed design procedure for achieving the optimal NN model and Principal Component Analysis for dimensionality reduction is proposed. Overall thirteen statistical parameters are used as feature space to achieve the desired classification. GFFD NN model is designed and verified for optimal performance in fault identification on experimental data set of custom designed 2 HP, three phase 50 Hz induction motor.

• 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).

• Journal of Korea Society of Digital Industry and Information Management
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• v.14 no.3
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• pp.39-44
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• 2018

Communication in real-time distributed system should have high reliability. To develop group communication Protocol with high reliability, potential fault should be known and when fault occurs, it should be detected and a necessary action should be taken. Existing detection method by Ack and Time-out is not proper for real time system due to load to Ack which is not received. Therefore, group communication messages from real-time distributed processing systems should be communicated to all receiving processors or ignored by the message itself. This paper can make be sure of transmission of reliable message and deadline by suggesting and experimenting fault detection technique applicable in the real time distributed system based on ring, and analyzing its results. The experiment showed that the shorter the cycle of the heartbeat signal, the shorter the time to propagate the fault detection, which is the time for other nodes to detect the failure of the node.

• Proceedings of the KIEE Conference
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• 1997.07c
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• pp.1012-1014
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• 1997

This paper introduces wavelets and shows that they may be efficient and useful for the detection of general faults in power system. The wavelet transform of a signal consists in measuring the "similarity" between the signal and a set of translated and scaled versions of a "mother wavelet". The "mother wavelet" is a chosen fast decaying oscillation function. A number of mother wavelet for signal analysis have been proposed and some of them are in use in fault detection. However, the performance of fault detection depend on used mother wavelet. In the present paper a comparative evaluation of different mother wavelets for low impedance fault detection is performed. The discussion is focused in well-known mother wavelet based wavelet transform. Several families of wavelets are used to analyse transient earth fault signals in a 345kV model system as generated by EMTP.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.1
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• pp.123-133
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• 1998

In this paper, an algorithm of fault detection and diagnosis during operation of induction motors under the condition of various loads and rates is investigated. For this purpose, the spectrum pattern of input currents is used in monitoring the state of induction motors, and by clustering the spectrum pattern of input currents, the newly occurrence of spectrum patterns caused by faults are detected. For the diagnosis of the fault detected, a fuzzy fault tree is designed, and the fuzzy relation equation representing the relation between an induction motor fault and each fault type, is solved. The solution of the fuzzy relation equation shows the possibility of occurence of each fault. The results obtained are summarized as follows : (1) Using clustering algorithm by unsupervised learning, an on-line fault detection method unaffected by the characteristics of loads and rates is implemented, and the degree of dependency for experts during fault detection is reduced. (2) With the fuzzy fault tree, the fault diagnosis process become systematic and expandable to the whole system, and the diagnosis for sub-systems can be made as an object-oriented module.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.19 no.7
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• pp.726-735
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• 2009

In this paper, we propose an approach to signal model-based fault detection and diagnosis system for induction motors. The current fault detection techniques used in the industry are limit checking techniques, which are simple but cannot predict the types of faults and the initiation of the faults. The system consists of two consecutive processes: fault detection process and fault diagnosis process. In the fault detection process, the system extracts the significant features from sound signals using combination of variance, cross-correlation and wavelet. Consequently, the pattern classification technique is applied to the fault diagnosis process to recognize the system faults based on faulty symptoms. The sounds generated from different kinds of typical motor's faults such as motor unbalance, bearing misalignment and bearing loose are examined. We propose two approaches for fault detection and diagnosis system that are waveletand-variance-based and wavelet-and-crosscorrelation-based approaches. The results of our experiment show more than 95 and 78 percent accuracy for fault classification, respectively.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.7
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• pp.1191-1198
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• 2007

In this paper, the fault detection and noise cancellation algorithm based on wavelet transform was developed to locate the fault more accurately. Specially, noise cancellation algorithm was based on the correlation of wavelet coefficients at multi-scales. Fault detection, classification and location algorithm were tested by EMTP simulation on real power cable system. From these results, the faults can be detected and located even in very difficult situations, such as at different inception angle and fault resistance.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10b
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• pp.1228-1233
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• 1990

This paper proposes a robust on-line fault detection method for uncertain systems. It is based on the fault detection method [10] accounting for modelling errors, which is shown to have superior performance over traditional methods but has some computational problems so that it is hard to be applied to on-line problems. The proposed method in this paper is an on-line version of the fault detection method suggested in [10]. Thus the method has the same detection performance robust to model uncertainties as that of [10]. Moreover, its computational burden is shown to be considerably lessened so that it is applicable to on-line fault detection problems.

• International Journal of Air-Conditioning and Refrigeration
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• v.11 no.3
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• pp.125-131
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• 2003

The fault detection and diagnosis technology may be applied in order to decrease the energy consumption and the maintenance cost of the air-conditioning system. In this paper, two fault detection methods were considered. One is a generic neural network, and the other is an moving average neural network. In order to compare the performance of fault detection results from these methods, two different types of faults in an air-conditioning system were applied. These are the condenser 30% fouling and the evaporator fan 25% slowdown. Test results showed that the moving average neural network was more effective for the detection of partial faults in the air-conditioning system.