Detecting broken rotor bars in induction motors with model-based support vector classifiers

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• 作者：Mohammed Obaid Mustafa^b ; ^{mohammed.obaid@ltu.se" class="auth_mail" title="E-mail the corresponding author} ; Damiano Varagnolo^a ; ^{damiano.varagnolo@ltu.se" class="auth_mail" title="E-mail the corresponding author} ; George Nikolakopoulos^a ; ^{george.nikolakopoulos@ltu.se" class="auth_mail" title="E-mail the corresponding author} ; Thomas Gustafsson^a ; ^{thomas.gustafsson@ltu.se" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Fault detection ; Model based methods ; Broken rotor bar ; Three phase asynchronous motors ; Statistical characterization ; Support vector classification ; Motor current signature analysis
• 刊名：Control Engineering Practice
• 出版年：2016
• 出版时间：July 2016
• 年：2016
• 卷：52
• 期：Complete
• 页码：15-23
• 全文大小：1119 K

文摘

We propose a methodology for testing the sanity of motors when both healthy and faulty data are unavailable. More precisely, we consider a model-based Support Vector Classification (SVC) method for the detection of broken bars in three phase asynchronous motors at full load conditions, using features based on the spectral analysis of the stator's steady state current (more specifically, the amplitude of the lift sideband harmonic and the amplitude at fundamental frequency). We diverge from the mainstream focus on using SVCs trained from measured data, and instead derive a classifier that is constructed entirely using theoretical considerations. The advantage of this approach is that it does not need training steps (an expensive, time consuming and often practically infeasible task), i.e., operators are not required to have both healthy and faulty data from a system for checking it. We describe what are the theoretical properties and fundamental limitations of using model based SVC methodologies, provide conditions under which using SVC tests is statistically optimal, and present some experimental results to prove the effectiveness of the suggested scheme.