期刊
MEASUREMENT
卷 131, 期 -, 页码 524-533出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.measurement.2018.09.013
关键词
Bearing fault diagnosis; Stockwell transform; Multi-class SVM; Feature selection; Three phase induction motor
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据