• Proceedings of the KIEE Conference
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• 1988.07a
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• pp.841-842
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• 1988

For determination of black-out area and restoration area by an expert system for fault section estimation and power system restoration using information from circuit breakers, it is necessary that the recognition of system isolation state and a method of finding the change of system isolation state by the state transition of breakers in isolated system. This paper presents a method of resolving the above problem by rule-based approach.

• Smart Structures and Systems
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• v.13 no.3
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• pp.453-471
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• 2014

The fault diagnosis of rolling element bearings has drawn considerable research attention in recent years because these fundamental elements frequently suffer failures that could result in unexpected machine breakdowns. Artificial intelligence algorithms such as artificial neural networks (ANNs) and support vector machines (SVMs) have been widely investigated to identify various faults. However, as the useful life of a bearing deteriorates, identifying early bearing faults and evaluating their sizes of development are necessary for timely maintenance actions to prevent accidents. This study proposes a new two-layer structure consisting of support vector regression machines (SVRMs) to recognize bearing fault patterns and track the fault sizes. The statistical parameters used to track the fault evolutions are first extracted to condense original vibration signals into a few compact features. The extracted features are then used to train the proposed two-layer SVRMs structure. Once these parameters of the proposed two-layer SVRMs structure are determined, the features extracted from other vibration signals can be used to predict the unknown bearing health conditions. The effectiveness of the proposed method is validated by experimental datasets collected from a test rig. The results demonstrate that the proposed method is highly accurate in differentiating between fault patterns and determining their fault severities. Further, comparisons are performed to show that the proposed method is better than some existing methods.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.9 no.4
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• pp.108-116
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• 2000

Undesired trouble such as chatter vibration and burning on the ground surface appears frequently in the cylindrical plunge grinding process. Establishment of a credible fault diagnostic system for the grinding process is the major purpose of this study. Power signals generated during the grinding operation were sampled and analyzed to determine the relationship between grinding troubles and behavior of signal changes. In addition, a neural network was optimized with a momentum coefficient a learning rate, and a structure of the hidden layer through the iterative learning process. Based on the established system, success rates of the trouble recognition were verified.

• Proceedings of the KIEE Conference
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• 2005.07a
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• pp.169-171
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• 2005

This paper presents a two-terminal approach for fault location estimation and for faults recognition. The proposed algorithm is also based on the synchronized phasors measured from the synchronized PMUs installed at two-terminals of the transmission lines. Also the arc voltage wave shape is modeled numerically on the basis of a great number of arc voltage records obtained by transient recorder. From the calculated arc voltage amplitude it can make a decision whether the permanent or transient fault. The results of the proposed algorithm testing through computer simulation are given.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.856-858
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• 1999

The analysis of distribution line faults is essential to the proper protection of power system. A high impedance fault(HIF) dose not make enough current to cause conventional protective device. It is well known that undesirable operating conditions and certain types of faults on electric distribution feeders cannot be detected by using conventional Protection system. This paper describes an algorithm using neural network for pattern recognition and detection of high impedance faults. Wavelet transform analysis gives the time-scale information. Time-scale representation of high impedance faults can detect easily and localize correctly the fault waveform.

• Tribology and Lubricants
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• v.18 no.2
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• pp.117-126
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• 2002

Abstract - An integrated condition monitoring algorithm based on an expert system was implemented in this work in order to monitor effectively the machine conditions. The knowledge base was consisted of numeric data which meant the posterior probability of each measurement parameter for the representative machine failures. Also the inference engine was constructed as a series of statistical process, where the probable machine fault was inferred by a mapping technology of pattern recognition. The proposed algorithm was, through the user interface, applied for an air compressor system where the temperature, vibration and wear properties were measured simultaneously. The result of the case study was found fairly satisfactory in the diagnosis of the machine condition since the predicted result was well correlated to the machine fault occurred.

• Proceedings of the KIEE Conference
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• 1997.07c
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• pp.897-899
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• 1997

The analysis of distribution line faults is essential to the proper protections of the power system. A high impedance fault does not make enough current to cause conventional protective devices. It is well known that undesirable operating conditions and certain types of faults on electric distribution feeders cannot be detected by using conventional protection system. This paper describes an algorithm using back-propagation neural network for pattern recognition and detection of high impedance faults. Fractal dimensions are estimated for distinction between random noise and chaotic behavior in the power system. The fractal dimension of the line current is also used as a indication of the high impedance fault.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.13 no.8
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• pp.787-795
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• 2001

An experimentation on the on-line fault detection and diagnosis(FDD) system has been performed with HVAC system in he experimental building constructed inside the large scale environmental chamber. Personal computer with a home-made FDD program by pattern recognition method utilizing artificial neural network was connected on-line via Ether-net TCP/IP to the supervisory control server for HVAC system. The FDD program monitored the HVAC system by 1 minuted interval. The results showed that he FDD program detected the sudden or abrupt faults such s those in fans, sensors and heater, etc.

• Smart Structures and Systems
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• v.19 no.2
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• pp.195-202
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• 2017

The aim of this paper is to develop a novel method to determine the severity of a damage in a thin plate. This paper presents a novel fault detection and diagnosis approach employing a new electromagnetic acoustic transducer, called EMAT, together with a complex signal processing method. The method consists in the recognition of a fault that exists within the structure, the fault location, i.e. the identification of the geometric position of damage, and the determining the significance of the damage, which indicates the importance or severity of the defect. The main scientific novelties presented in this paper is: to develop of a new type of electromagnetic acoustic transducer; to incorporate wavelet transforms for signal representation enhancements; to investigate multi-parametric analysis for noise identification and defect classification; to study attenuation curves properties for defect localization improvement; flaw sizing and location algorithm development.

• Smart Structures and Systems
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• v.14 no.3
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• pp.377-395
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• 2014

Vibration-based fault detection and condition monitoring of rotating machinery, using statistical process control (SPC) combined with statistical pattern recognition methodology, has been widely investigated by many researchers. In particular, the discrete wavelet transform (DWT) is considered as a powerful tool for feature extraction in detecting fault on rotating machinery. Although DWT significantly reduces the dimensionality of the data, the number of retained wavelet features can still be significantly large. Then, the use of standard multivariate SPC techniques is not advised, because the sample covariance matrix is likely to be singular, so that the common multivariate statistics cannot be calculated. Even though many feature-based SPC methods have been introduced to tackle this deficiency, most methods require a parametric distributional assumption that restricts their feasibility to specific problems of process control, and thus limit their application. This study proposes a nonparametric multivariate control chart method, based on multiscale wavelet scalogram (MWS) features, that overcomes the limitation posed by the parametric assumption in existing SPC methods. The presented approach takes advantage of multi-resolution analysis using DWT, and obtains MWS features with significantly low dimensionality. We calculate Hotelling's $T^2$-type monitoring statistic using MWS, which has enough damage-discrimination ability. A bootstrap approach is used to determine the upper control limit of the monitoring statistic, without any distributional assumption. Numerical simulations demonstrate the performance of the proposed control charting method, under various damage-level scenarios for a bearing system.