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Improved Classification by Non Iterative and Ensemble Classifiers in Motor Fault DiagnosisPANIGRAHY, P. S.![]() ![]() ![]() ![]() ![]() ![]() |
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Author keywords
discrete wavelet transforms, fault diagnosis, feature extraction, induction motors, machine learning
References keywords
induction(17), fault(13), diagnosis(9), motors(8), detection(8), motor(7), analysis(7), wavelet(6), vibration(5), mining(5)
Blue keywords are present in both the references section and the paper title.
About this article
Date of Publication: 2018-02-28
Volume 18, Issue 1, Year 2018, On page(s): 95 - 104
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2018.01012
Web of Science Accession Number: 000426449500012
SCOPUS ID: 85043281619
Abstract
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. |
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Stefan cel Mare University of Suceava, Romania
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