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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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  1/2018 - 12

Improved Classification by Non Iterative and Ensemble Classifiers in Motor Fault Diagnosis

PANIGRAHY, P. S. See more information about PANIGRAHY, P. S. on SCOPUS See more information about PANIGRAHY, P. S. on IEEExplore See more information about PANIGRAHY, P. S. on Web of Science, CHATTOPADHYAY, P. See more information about CHATTOPADHYAY, P. on SCOPUS See more information about CHATTOPADHYAY, P. on SCOPUS See more information about CHATTOPADHYAY, P. on Web of Science
 
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Download PDF pdficon (8,079 KB) | Citation | Downloads: 538 | Views: 2,734

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
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Full text preview
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.


References | Cited By  «-- Click to see who has cited this paper

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References Weight

Web of Science® Citations for all references: 3,362 TCR
SCOPUS® Citations for all references: 6,563 TCR

Web of Science® Average Citations per reference: 120 ACR
SCOPUS® Average Citations per reference: 234 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2021-06-23 11:07 in 175 seconds.




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