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Decent fault classification of VFD fed induction motor using random forest algorithm

Published online by Cambridge University Press:  20 July 2020

Parth Sarathi Panigrahy Open the ORCID record for Parth Sarathi Panigrahy [Opens in a new window] ,
Deepjyoti Santra Open the ORCID record for Deepjyoti Santra [Opens in a new window]  and
Paramita Chattopadhyay Open the ORCID record for Paramita Chattopadhyay [Opens in a new window]
Parth Sarathi Panigrahy*
Affiliation:
Department of Electrical and Electronics Engineering, Madanapalle Institute of Technology & Science, Madanapalle, Andhra Pradesh, India
Deepjyoti Santra
Affiliation:
Department of Electrical Engineering, Global Institute of Management and Technology, Krishnanagar, West Bengal, India
Paramita Chattopadhyay
Affiliation:
Department of Electrical Engineering, Indian Institute of Engineering Science and Technology, Shibpur, West Bengal, India
*
Author for correspondence: Parth Sarathi Panigrahy, E-mail: parth.panigrahy@gmail.com
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Abstract

A data-driven approach for multiclass fault diagnosis of drive fed induction motor (IM) using stator current at steady-state condition is a complex pattern classification problem. The applied DWT-IDWT algorithm in this work is reinforced by a novel selection criterion for mother wavelet application and justifies the originality of the work. This investigation has exploited the built-in feature selection process of Random Forest (RF) classifier to resolve the most challenging issues in this area, including bearing and stator fault detection. RF has shown an outstanding performance without application of any feature selection technique because of its distributive feature model. The robustness of the results backed by the experimental verification shows an encouraging future of RF as a classifier in the area of intelligent fault diagnosis of IM.

Keywords

Discrete wavelet transformsfault diagnosisinduction motorrandom forestvariable frequency drive
Type
Research Article
Information
AI EDAM , Volume 34 , Issue 4 , November 2020 , pp. 492 - 504
Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press

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