Author: Lau, Chi-chuen Enzo
Title: Application of wavelet packet transformation for motor current signature analysis
Degree: Eng.D.
Year: 2009
Subject: Hong Kong Polytechnic University -- Dissertations.
Electric motors, Induction -- -- Mathematical models.
Electric fault location -- Mathematical models.
Wavelets (Mathematics)
Fault location (Engineering) -- Mathematical models.
Department: Department of Electrical Engineering
Pages: xviii, 182 leaves : ill. ; 30 cm.
Language: English
Abstract: Buildings nowadays are fitted with lots of building services system for their normal operation, Induction motors are the most commonly adopted prime mover of these building services systems and the breakdown of these building services systems are usually associated with the motor fault. Although there are numerous types of motor fault, based on a research conducted by Electric Power Research Institute (EPRI), motor bearing fault accounted for more than 40% of all type of motor faults. To ensure the reliability of the motors, planned preventive maintenance is carried out in the whole life cycle of a facility to reveal any symptoms of failure or breakdown. To further enhance the reliability, Condition Based Monitoring (CBM) techniques are usually adopted. Vibration analysis, Axial flux detection, Lubricating oil debris analysis, partial discharge analysis and Motor Current Signature analysis (MCSA) are the usually adopted CBM techniques for detection of the failure of motor-driven building services systems. Motor faults usually to be detected are Insulation Breakdown, Stator Winding Fault, Air-gap Eccentricity Fault, Broken Rotor Bar Fault and Bearing Damages. Besides the Insulation Breakdown fault, MCSA is applicable to all other motor fault detection which makes it an ideal solution to form a core detection technique for CBM of motor fault. MCSA is a method of sampling the running current through a data logger at high sampling speed, followed by the use of Fast Fourier Transform (FFT) to identify relevant motor signature in the frequency spectrum for motor fault identification. However, drawback of using FFT for MCSA is the limitation of applying it to stationary waveforms only. Main aim of this thesis is to evaluate the feasibility of using Wavelet Packet Transform (WPT) to identify signature change of motors having bearing outer race defects. This is achieved by fulfilling the following stage by stage objectives:- (1) By applying Wavelet Packet Transformation (WPT) integrating FFT technique to extract exact frequency content of the running current in each wavelet packet transformed reconstruction data. (2) Applying the principle of energy conservation to identify the fingerprint of the running current to judge the condition of the motor bearing. To show its feasibility, WPT for MCSA has been tested in stages and successfully detects motor bearing outer raceway defect.
Rights: All rights reserved
Access: restricted access

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