Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/42887
Title: Fault detection and prognostic of abnormal equipment situations using wavelet decomposition technique
Authors: Ling, Wee Kee
Keywords: DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation
Issue Date: 2011
Abstract: Unplanned system failure results in high cost and liabilities for system operators. The implementation of effective prognostics systems would allow identification of faults before the actual occurrence of critical failure thereby avoiding and mitigating system failures. This project focuses on identifying fault characteristics of Marathon Electric AC Induction motor. Broken Rotor Bar, Motor Bearing and Rotor Unbalance Faults would be presented in detail. Wavelet Packet Decomposition is used to extract a windowed frequency from the vibration signal for Bearing and Unbalance fault. Broken Rotor Bar faults are diagnosed using a new approach. A system is created to train no fault signals and then tested with an unknown fault signal. Analysis is conducted to extract the characteristic fault frequencies and conduct a comparison with the no fault signal counterpart. Bearing and Unbalance fault are successfully identified; however, Broken Rotor Bar's experiments do not tally with the findings in the existing literature. An alternative method has been utilized. The software system that is developed provides fairly good fault identification capabilities.
URI: http://hdl.handle.net/10356/42887
Rights: Nanyang Technological University
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

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