Journal
MEASUREMENT
Volume 131, Issue -, Pages 524-533Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.measurement.2018.09.013
Keywords
Bearing fault diagnosis; Stockwell transform; Multi-class SVM; Feature selection; Three phase induction motor
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This paper presents faulty bearing detection, classification and its location in a three-phase induction motor using Stockwell transform and Support vector machine. Stockwell transform is applied to stator current signals to extract a number of features in both time and frequency domain. A set of non-correlated and high ranking features are selected based on Fisher score ranking. These features are in turn used to classify the faults such as ball, cage and outer-race faults using Support vector machine. Subsequent to fault identification, features of Stockwell transform are used to locate the defective bearing, i.e, either at fan-side or load-side of the motor. This algorithm is successfully implemented on the experimental data of defective bearings collected from the industry. (C) 2018 Elsevier Ltd. All rights reserved.
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