Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/35808
Title: ADAPTIVE FAULT DETECTION AND CONDITION MONITORING OF INDUCTION MOTOR
Authors: LU WENJING
Keywords: induction motor, broken rotor bar, bearing fault, fault detection, wavelet, wavelet packet
Issue Date: 23-Nov-2012
Source: LU WENJING (2012-11-23). ADAPTIVE FAULT DETECTION AND CONDITION MONITORING OF INDUCTION MOTOR. ScholarBank@NUS Repository.
Abstract: This research is focused upon the investigation of the two specific types of induction motor faults: broken rotor bar fault and bearing fault. They are the most frequently occurring faults in industries. The goal is to develop appropriate algorithms for the perspective of on-line detection and diagnosis of these faults measured on two laboratory motors. In the framework of the present thesis, faults occurring on these motors have been studied in details both theoretically and numerically. Although fault-related features can be observed directly on the frequency spectrum derived from time-domain measurements of stator currents, a good feature extraction strategy and quantification method will reduce the human effort and surely improve the reliability and convenience of online fault detection. Hence, the candidate proposes two techniques namely Adaptive Centered Wavelet Technique (ACWT) and Adaptive Wavelet Packet Technique (AWPT) to achieve an adaptive feature extraction for motor stator currents under different inverter frequencies.
URI: http://scholarbank.nus.edu.sg/handle/10635/35808
Appears in Collections:Master's Theses (Open)

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