• 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.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.12
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• pp.1090-1095
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• 2012

In this paper, we propose a model-based fault diagnosis method applied to vibration data. The fault detection is performed by comparing estimated parameters with normal parameters and deciding if the observed changes can be explained satisfactorily in terms of noise or undermodelling. The key feature of this method is that it accounts for the effects of noise and model mismatch. And we aslo design a classifier for the fault isolation by applying the multiclass SVM (Support Vector Machine) to the estimated parameters. The proposed fault detection and isolation methods are applied to an engine vibration data to show a good performance. The proposed fault detection method is compared with a signal-based fault detection method through a performance analysis.

• Journal of Power Electronics
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• v.10 no.6
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• pp.686-693
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• 2010

In this paper, a fault diagnosis method based on fuzzy logic for the three-parallel power converter in a wind turbine system is presented. The method can not only detect both open and short faults but can also identify faulty switching devices without additional voltage sensors or an analysis modeling of the system. The location of a faulty switch can be indicated by six-patterns of a stator current vector and the fault switching device detection is achieved by analyzing the current vector. A fault tolerant algorithm is also presented to maintain proper performance under faulty conditions. The reliability of the proposed fault detection technique has been proven by simulations and experiments with a 10kW simulator.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2005.11a
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• pp.158-161
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• 2005

In this study, the defect diagnosis of the gas turbine engine was tried using Support Vector Machine(SVM). It is known that SVM can find the optimal solution mathematically through classifying two groups and searching for the Hyperplane of the arbitrary nonlinear boundary. The method for the decision of the gas turbine defect quantitatively was proposed using the Multi Layer SVM for classifying two groups and it was verified that SVM was shown quicker and more reliable diagnostic results than the existing Multi Layer Perceptron(MLP).

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.4
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• pp.408-413
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• 2014

In this paper, we propose a fault diagnosis method based on Park's Vector Approach using the Euler's theorem. If we interpreted it as Euler's theorem, it is possible to easily find the phase angle difference between the healthy condition and the fault condition. And, we analyzed the variation of the phase angle and performed the diagnostic method of the induction motor using feature vectors that were obtained by using a Fourier transform. The analysis of time and speed variation of the motor was performed and, as a result, we could find more soft variations than rough variations. In particular, the analysis of the distortion through each phase shows that two-turn and four-turn shorted motors are linearly separable. In this experiment, we know that the maximum breakdown threshold value for determining steady-state fault detection is 49.0788. Simulation and experimental results show the more detectable than conventional method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.6
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• pp.1291-1296
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• 2003

This paper deals with efficient diagnostic for stator winding fault of 3-phase induction motor using a current Park's vector approach. This method firstly transforms 3-phase stator current to vertical axis current and horizontal axis current of Park's Vector, and then obtains the each Park's Vector Pattern and detects stator winding fault by comparing to Park's Vector Pattern of healthy and fault. Experimental results, obtained by using induction motor having inter-turn fault of 2, 10, 20 turn, demonstrate the effectiveness of the proposed technique, for detecting the presence of stator winding fault under 25%, 50%, and 100% of full load condition.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2070-2072
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• 2003

본 연구는 3상 소형 유도전동기의 고정자 권선 고장의 효과적인 진단을 위하여 고정자 전류에 대하여 Park's Vector를 이용한 기법을 적용하였다. 본 기법은 고정자 3상 전류를 측정하여 Park's vector 변환을 통하여 직축, 횡축 전류로 변환하고 이를 이용하여 고장 진단을 위한 Park's Vector Pattern을 통하여 고장진단을 수행하였다. 제안한 방법의 유용성을 확인하기 위하여 고정자 권선 한 상에 2턴, 10턴, 그리고 20턴의 단락고장을 발생시켜 정격부하의 25%, 50%, 100%에 대하여 부하변동에 따른 각각의 단락고장의 경우와 정상 전동기의 Park's Vector Pattern 비교하였으며 그 유용성을 확인하였다.

• Proceedings of the KIEE Conference
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• 2005.07d
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• pp.2658-2660
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• 2005

This paper presents a stator winding fault diagnosis technique of induction motor on the PC - based virtual instrumentation system designed using the graphical programming language LabVIEW. This method collects the 3-phase current signals using the current probe amplifier and PXI/DAQ system then the preprocessing removes the noise using LPF, after then this method transforms the stator current to Park's vector and obtains the each Park's Vector pattern and detects stator winding fault by comparing the obtained faulted pattern with the healthy pattern. This proposed LabVIEW based diagnosis system is applied to the 3 phase 1 hp induction motor and obtained the reasonable results under no load condition. The test results give us the possibility a simple and realistic on-line winding fault diagnosis system.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.9
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• pp.4455-4461
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• 2013

This paper investigates the diagnosis performance of Artificial Neural Network (ANN) depending on the structure and the input vector type of ANN, which has been used to detect the partial discharge to lead to the electric machinery deterioration. The diagnosis performance of one hidden layer and two hidden layer in ANN are compared. The performance using the 2048 time-series data and the performance using the feature input vector are compared. For measuring the partial discharge signal, the tip-to-plate, the sphere-to-sphere, the tip-to-tip, the tip-to-sphere and the sphere-to-plate electrodes are used respectively. For ANN's learning, Matlab and C-code program are used. For evaluating the diagnosis performance of ANNs, the simulation studies are performed.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.2
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• pp.35-44
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• 1994

In this paper, an observer for linear discrete systems with unknown inputs is presented. The suggested observer can estimate the system state vector and the unknown inputs simultaneously. As an extension of the observer, a new fault diagnosis observer for linear discrete systems with structured uncertainty is presented. The fault diagnosis observer can detect and identify the actuator and the sensor faults as well. The stability conditionsand the design methods of the each observers are presented and the usability of the observers is shown via numerical examples.