Bearing fault detection of induction motor using wavelet and Support Vector Machines (SVMs)
Bearing fault detection of induction motor using wavelet and Support Vector Machines (SVMs)
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DOI：
10.1016/j.asoc.2011.03.014
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发表时间：
2011-09-01
影响因子：
8.7
通讯作者：
Chattopadhyay, P.
中科院分区：
文献类型：
--
作者：
Konar, P.;Chattopadhyay, P.
感应电机状态监测是电气设备维修领域中一项快速发展的技术，由于可以避免关键系统发生意外故障的次数，因此在世界范围内受到越来越多的关注。考虑到这一点，本文尝试了一种三相感应电机轴承故障检测方案。本文将支持向量机与先进的信号处理工具--连续小波变换相结合，用来分析车架在启动过程中的振动。连续小波变换在状态监测领域还没有得到广泛的应用，但与广泛使用的基于离散小波变换的方法相比，可以获得更好的结果。本文的分析结果可望为简单、快速、克服传统基于数据的模型/技术的局限性的感应电机状态监测技术奠定基础。(C)2011爱思唯尔B.V.保留所有权利。
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 failure of a critical system can be avoided. Keeping this in mind a bearing fault detection scheme of three-phase induction motor has been attempted. In the present study, Support Vector Machine (SVM) is used along with continuous wavelet transform (CWT), an advanced signal-processing tool, to analyze the frame vibrations during start-up. CWT has not been widely applied in the field of condition monitoring although much better results can been obtained compared to the widely used DWT based techniques. The encouraging results obtained from the present analysis is hoped to set up a base for condition monitoring technique of induction motor which will be simple, fast and overcome the limitations of traditional data-based models/techniques. (C) 2011 Elsevier B.V. All rights reserved.