Fault Detection Of Motors Using Wavelets And Machine Learning

Sim, Kai Jet (2018) Fault Detection Of Motors Using Wavelets And Machine Learning. Final Year Project (Bachelor), Tunku Abdul Rahman University College.

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Abstract

Condition monitoring of induction motors is a fast emerging technology in the field of electrical equipment maintenance and has attracted more and more attention worldwide as the number of unexpected failures of a critical system can be avoided. Based on data given, bearing fault detection scheme of three-phase induction motor was happen majority. In the present study, Neural Network Pattern Recognition (NPR) is used along with continuous wavelet transform (CWT), an advanced signal-processing tool, to analyze the motor current. CWT has not been widely applied in the field of condition monitoring although much better results can be obtained compared to the widely used discrete wavelet transform (DWT) based techniques. The results obtain from the fault detection analysis is hope that able to get fault will simple, fast and overcome the limitations of traditional data-based models/techniques.