• Journal of Electrical Engineering and Technology
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• v.8 no.5
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• pp.1116-1121
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• 2013

Mechanical imbalances are common mechanical faults in induction motors. Vibration monitoring techniques have been widely used for the diagnosis of mechanical faults in induction motors, but electrical detection methods have been preferred in recent years. For many years, researchers have concentrated on the Motor Current Signature Analysis (MCSA). This paper examines the effect of mechanical imbalances to induction machine electrical parameters. Instantaneous Power Signature Analysis (IPSA) technique used to detect these faults. In the paper, a full analysis of the proposed technique is presented, and experimental results for healthy and faulty motors have been shown and discussed.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2175-2177
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• 2001

This paper presents a faults diagnosis technique of induction motors based on a neural network. Only stator current is measured, transformed by using FFT and normalized for the training. Healthy, bearing fault, stator fault and rotor end-ring fault motors are prepared to obtain the learning data and diagnose the several faults. For more effective diagnosis, the load rate is changed by 100%, 60%, 30% of full load and the obtained are applied to the learning process. The experimental results show the proposed method is very detectable and applicable to the real diagnosis system.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.11
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• pp.1657-1662
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• 2017

Rotor bar faults in induction machines, which are a part of main distribution of power system, can even stop the entire system by causing contact between a stator and a rotor. There are two methods of diagnosing rotor bar faults in induction motors, online and offline tests, and existing diagnosis methods have many limitations which can lead to misdiagnosis. This paper proposes a potable rotor bar faults diagnosis system based on single phase rotation test, one of offline test methods, which detects rotor bar faults through impedance interpretation by exciting AC current in a stator winding. The test was conducted on a motor of 0.4kW in the laboratory and a motor of 1500kW in industry field.

• 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.

• Proceedings of the KSME Conference
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• 2004.04a
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• pp.822-827
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• 2004

In this paper, an intelligent fault diagnosis system is proposed for induction motors through the combination of feature extraction, genetic algorithm (GA) and neural network (ANN) techniques. Features are extracted from motor vibration signals, while reducing data transfers and making on-line application available. GA is used to select most significant features from whole feature database and optimize the ANN structure parameter. Optimized ANN diagnoses the condition of induction motors online after trained by the selected features. The combination of advanced techniques reduces the learning time and increases the diagnosis accuracy. The efficiency of the proposed system is demonstrated through motor faults of electrical and mechanical origin on the induction motors. The results of the test indicate that the proposed system is promising for real time application.

• Journal of Electrical Engineering and Technology
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• v.5 no.1
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• pp.140-145
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• 2010

The monitoring and the diagnosis of the faults in induction motors starting from the stator current are very interesting, since it is an accessible and measurable quantity. The spectral analysis of the stator current makes it possible to highlight the characteristic frequencies of the faults but in a wide frequency range depending on half the sampling frequency, making it very difficult to monitor on-line the faults. In order to facilitate the use of the relevant frequencies of machine faults we proposed the extraction of the frequency components using two methods, namely, the amplitude and the instantaneous frequency. The theoretical bases of these methods were presented and the results were validated on a test bench with an induction motor of 5.5 kw.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2013.10a
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• pp.338-345
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• 2013

Induction motors play an increasing importance in industrial manufacturing. Therefore, the state monitoring systems also have been considering as the key in dealing with their negative effect by absorbing faulty symptoms in motors. There are numerous proposed systems in literature, in which, several kinds of signals are utilized as the input. To solve the multiple faults problem of induction motors, like the proposed system, the vibration signals is good candidate. In this study, a new signal processing scheme was utilized, which transforms the time domain vibration signal into the spatial domain as an image. Then the spatial features of converted image then have been extracted by applying the dominant neighbourhood structure (DNS) algorithm. In addition, these feature vectors were evaluated to obtain the fruitful dimensions, which support to discriminate between states of motors. Because of reliability, the conventional one-against-all (OAA) multi-class support vector machines (MCSVM) have been utilized in the proposed system as classifier module. Even though examined in severity levels of signal-to-noise ratio (SNR), up to 15dB, the proposed system still reliable in term of two criteria: true positive (TF) and false positive (FP). Furthermore, it also offers better performance than five state-of-the-art systems.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.6
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• pp.819-825
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• 2007

In this paper, we propose a fault diagnosis algorithm of induction motors by DFT and wavelet. We extract a feature vector using a fault pattern extraction method by DFT in frequency domain and wavelet transform in time-frequency domain. And then we deal with a fusion algorithm for the feature vectors extracted from DFT and wavelet to classify the faults of induction motors. Finally, we provide an experimental results that the proposed algorithm can be successfully applied to classify the several fault signals acquired from induction motors.

• Journal of Power Electronics
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• v.8 no.2
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• pp.181-191
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• 2008

The task performed by induction motors grows increasingly complex in modern industry and hence improvements are sought in the field of fault diagnosis. It is essential to diagnose faults at their very inception, as unscheduled machine down time can upset critical dead lines and cause heavy financial losses. Artificial intelligence (AI) techniques have proved their ability in detection of incipient faults in electrical machines. This paper presents an application of AI techniques for the detection of inter-turn insulation and bearing wear faults in single-phase induction motors. The single-phase induction motor is considered a proto type model to create inter-turn insulation and bearing wear faults. The experimental data for motor intake current, rotor speed, stator winding temperature, bearing temperature and noise of the motor under running condition was generated in the laboratory. The different types of fault detectors were developed based upon three different AI techniques. The input parameters for these detectors were varied from two to five sequentially. The comparisons were made and the best fault detector was determined.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.14 no.11
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• pp.1091-1100
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• 2004

This paper presents an approach for the monitoring and detection of faults in induction machine by using data fusion technique and Dempster-Shafer theory Features are extracted from motor stator current and vibration signals. Neural network is trained and Hosted by the selected features of the measured data. The fusion of classification results from vibration and current classifiers increases the diagnostic accuracy. The efficiency of the proposed system is demonstrated by detecting motor electric and mechanical faults originated from the induction motors. The results of the test confirm that the proposed system has potential for real time application.