• Transactions of the Korean Society of Automotive Engineers
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• v.13 no.1
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• pp.126-131
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• 2005

This paper presents experimental results by using the machinery fault simulator which is monitoring its conditions with an acceleration signal. Components of the machinery, for example, motor, belt pulley, belt, bearing, and gear, with artificial defects were used for the experiment.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.6
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• pp.635-640
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• 2014

Detection and diagnosis of incipient bearing fault in an induction motor is important for the prevention of serious motor failure. This paper presents an analysis of the effect of a faulty bearing on the stator current of an induction motor. A bearing fault leads to torque oscillations which result in phase modulation of the stator current. Since the torque oscillations cause specific frequency components at the stator current spectrum to rise sharply, the bearing fault can be detected by checking out the faultrelated frequency. In this paper, a mathematical model of the load torque oscillation caused by a bearing fault is presented. The proposed model can be used to analyze the physical phenomenon of a bearing fault in an induction motor. In order to represent the bearing fault effect, the proposed model is combined with an existing model of vector-controlled induction motors. A set of simulation results demonstrate the effectiveness of the proposed model and represent that bearing fault detection using a stator current is useful for vector-controlled induction motors.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.22 no.12
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• pp.1227-1236
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• 2012

The low speed machinery faults are usually caused by the bearing failure of the rolling elements. As the life time of the bearing is limited, the condition monitoring of bearing is very important to maintain the continuous operation without failures. A few monitoring techniques using time domain, frequency domain and fuzzy neural network vibration analysis are introduced to detect and diagnose the faults of the low speed machinery. This paper presents a method of fault detection for the rolling element bearing in the low speed machinery using the Wiener filtering and shock pulse counting techniques. Wiener filter is used for noise cancellation and it clearly makes the shock pulse emerge from the time signal with the high level of noise. The shock pulse counting is used to determine the various faults obviously from the shock signal with transient pulses not related with the bearing fault. Machine fault simulator is used for the experimental measurement in order to verify this technique is the powerful tool for the low speed machine compared with the frequency analysis. The test results show that the method proposed is very effective parameter even for the signal with high contaminated noise, speed variation and very low energy. The presented method shows the optimal tool for the condition monitoring purpose to detect the various bearing fault with high accuracy.

• Proceedings of the KIEE Conference
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• 2006.04b
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• pp.182-184
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• 2006

A down-scaled simulator is developed to simulate typical faults in induction motor such as short-turn stator winding, broken rotor bar, dynamic and static air-gap eccentricity, bearing trouble, and mechanical unbalance. The simulator is used as an initial builder to develop design algorithm for real-time faults detecting system by processing an abnormal signal and characteristics in each fault.