文章基本信息

标题：Improved Classification by Non Iterative and Ensemble Classifiers in Motor Fault Diagnosis
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作者：P. S. PANIGRAHY ; P. CHATTOPADHYAY
期刊名称：Advances in Electrical and Computer Engineering
印刷版ISSN：1582-7445
电子版ISSN：1844-7600
出版年度：2018
卷号：18
期号：1
页码：95-104
DOI：10.4316/AECE.2018.01012
出版社：Universitatea "Stefan cel Mare" Suceava
摘要：Data driven approach for multi-class fault diagnosis of induction motor using MCSA at steady state condition is a complex pattern classification problem. This investigation has exploited the built-in ensemble process of non-iterative classifiers to resolve the most challenging issues in this area, including bearing and stator fault detection. Non-iterative techniques exhibit with an average 15% of increased fault classification accuracy against their iterative counterparts. Particularly RF has shown outstanding performance even at less number of training samples and noisy feature space because of its distributive feature model. The robustness of the results, backed by the experimental verification shows that the non-iterative individual classifiers like RF is the optimum choice in the area of automatic fault diagnosis of induction motor.