• 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.

• Journal of Electrical Engineering and Technology
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• v.6 no.5
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• pp.666-670
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• 2011

In this paper, a method for detecting broken rotor bar and stator winding fault in a low voltage squirrel-case induction motor using an air-gap flux variation analysis is proposed to develop a simple and low cost diagnosis technique. To measure the leakage flux in radial direction, a radial flux sensor is designed as a search coil and installed between stator slots. The proposed method is able to identify two kinds of motor faults by calculating load condition of motors and monitoring abnormal signals those are related with motor faults. Experimental results obtained on 7.5kW three-phase squirrel-cage induction motors are discussed to verify the performance of the proposed method.

• Journal of the Korean Magnetics Society
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• v.6 no.5
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• pp.287-293
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• 1996

The squirrel cage rotors of induction motors may have several faults such as broken bars, bad spots in end ring and abnormal skew caused by improper processing. These faults may cause bad effects on the performance of the induction motor. This paper proposes the detecting technique of these faults by analyzing the induced current of the detecting electric magnet, using 2-D finite element method taking account of the rotor movement.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.11a
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• pp.549-555
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• 2003

The technology of machine condition monitoring is used effectively to detect the machine faults at an early stage using different machine quantities, such as current, voltage, temperature and vibration. Induction motors are most widely used to drive pumps, compressors and fans in industrial drives. This paper presents approach to data fusion using Dempster-Shafer theory because only one technique has uncertainty. So we can obtain advanced accuracy of the machine fault diagnosis. Vibration and current quantities are applied to diagnose three-phase induction motor.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2006.11a
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• pp.46-53
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• 2006

This paper presents the fault diagnosis of induction motor based on support vector machine (SVMs). SVMs are well known as intelligent classifier with strong generalization ability. Application SVMs using kernel function is widely used for multi-class classification procedure. In this paper, the algorithm of SVMs will be combined with feature extraction and reduction using component analysis such as independent component analysis, principal component analysis and their kernel (KICA and KPCA). According to the result, component analysis is very useful to extract the useful features and to reduce the dimensionality of features so that the classification procedure in SVM can perform well. Moreover, this method is used to induction motor for faults detection based on vibration and current signals. The results show that this method can well classify and separate each condition of faults in induction motor based on experimental work.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.7
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• pp.492-498
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• 2016

It is well known that since abrupt faults in induction motors tend to lead to subsequent faults and deterioration of the drive apparatus, motor faults may lead to several operating restrictions, such as security problems and economic loss. A lot of research has been done in the area of diagnosis to detect machine faults and to prevent catastrophic hazards in the motor drive system. This paper presents a new method of motor current signature analysis in which the DC-link current of the inverter-driven induction motor system, where a single current sensor is employed instead of three AC current sensors, is measured, and fast Fourier transform analysis is performed. This proposed method makes it possible to easily discern and clearly separate the motor fault current signature from the normal operation current flowing through the stator and rotor windings.

• 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.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2006.11a
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• pp.411-416
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• 2006

Fault detection and diagnosis is the most important technology in condition-based maintenance(CBM) system that usually begins from collecting signatures of running machines using multiple sensors for subsequent accurate analysis. With the quick development in industry, there is an increasing requirement of selecting special sensors that are cheap, robust, and easy-installation. This paper experimentally investigated performances of four types of sensors used in induction motors faults diagnosis, which are vibration, current, voltage and flux. In addition, diagnostic effects of five popular classifiers also were evaluated. First, the raw signals from the four types of sensors are collected at the same time. Then the features are calculated from collected signals. Next, these features are classified through five classifiers using artificial intelligence techniques. Finally, conclusions are given based on the experiment results.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2006.11a
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• pp.54-59
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• 2006

Decision tree is one of the most effective and widely used methods for building classification model. Researchers from various disciplines such as statistics, machine learning, pattern recognition, and data mining have considered the decision tree method as an effective solution to their field problems. In this paper, an application of decision tree method to classify the faults of induction motors is proposed. The original data from experiment is dealt with feature calculation to get the useful information as attributes. These data are then assigned the classes which are based on our experience before becoming data inputs for decision tree. The total 9 classes are defined. An implementation of decision tree written in Matlab is used for these data.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.16 no.4 s.109
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• pp.404-413
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• 2006

Induction motors are the workhorse of our industry because of their versatility and robustness. The diagnosis of mechanical load and power transmission system failures is usually carried out through mechanical signals such as vibration signatures, acoustic emissions, motor speed envelope. The motor faults including mechanical rotor imbalances, broken rotor bar, bearing failure and eccentricities problems are reflected in electric, electromagnetic and mechanical quantities. The recent research has been directed toward electrical monitoring of the motor with emphasis on inspecting the stator current of the motor, The stator current spectrum has been widely used for fault detection in induction motor systems. The motor current signature analysis is the useful technique to assess machine electrical condition. This paper describes the motor condition detected by the current signatures Paralleled with vibration signatures analysis of induction motors with the roller bearing and the journal bearing type for large vertical pumps in power plant as examples to discuss for motor fault detection and diagnosis.