Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/36107
Title: Application of Computational Intelligence for Faulty Detection and Diagnosis of Induction Motors and electromechanical Systems
Authors: ZHANG YIFAN
Keywords: Faulty detection, diagnosis, FFT, Envelope Analysis, ICA, SVM
Issue Date: 19-Sep-2012
Source: ZHANG YIFAN (2012-09-19). Application of Computational Intelligence for Faulty Detection and Diagnosis of Induction Motors and electromechanical Systems. ScholarBank@NUS Repository.
Abstract: Nowadays, induction motors are widely used in industrial drives. However, predictive detection and diagnosis of mechanical faults of an induction motor is a challenging problem. To achieve automatic diagnosis and detection aim, it is crucial to establish understanding of early fault diagnosis. In this study, we apply the ICA method, extracts features obtained from the results of FFT-En method, and then uses SVM method separate these features from each other in the feature space. This study explores an automatic way to monitor and diagnosis induction motors and electromechanical systems instead of manual diagnosis.
URI: http://scholarbank.nus.edu.sg/handle/10635/36107
Appears in Collections:Master's Theses (Open)

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