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research-article

Multi Sensor based Bearing Fault Diagnosis of Switched Reluctance Motor for Electric Vehicle

Authors:

Anurag Choudhary,

Bijaya Ketan PanigrahiAuthors Info & Claims

ICECC '23: Proceedings of the 2023 6th International Conference on Electronics, Communications and Control Engineering

Pages 257 - 263

https://doi.org/10.1145/3592307.3592348

Published: 14 August 2023 Publication History

Abstract

Electric Vehicles (EVs) are extremely efficient and produce zero emissions which is a better alternative than conventional IC engine vehicles. The electric motor is the heart of every EV and is the key to realizing the optimum balance of top speed, acceleration, deceleration, and achievable distance per charge. In such conditions, the continuous monitoring of EV motors demands efficient methods to avoid catastrophic and vital loss. This paper proposed a multi-sensor-based approach for bearing fault diagnosis of an electric vehicle motor, i.e., a Switched Reluctance Motor (SRM), under constant and varying speed conditions. Initially, raw acoustic and vibration data are acquired at constant and varying speed conditions and decomposed by the Hilbert transform, followed by feature extraction. Thereafter, a Bayesian optimized Neural Network (BoNN) has been proposed for evaluating the performance of individual sensors using five bearing conditions of the SRM. The experimental findings depict that the proposed strategy involving different modality sensors provides promising and reliable results with a maximum accuracy of 100 %.

References

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Digital Library

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Cited By

Lorencki JRadkowski S(2024)Mechanical Faults Analysis in Switched Reluctance MotorApplied Sciences10.3390/app1408345214:8(3452)Online publication date: 19-Apr-2024
https://doi.org/10.3390/app14083452
Mohammad-alikhani AMahmouditabar FBaker NNahid-Mobarakeh B(2024)Data-Driven Inter Turn Short Circuit Fault Detection of a Segmented SRM Based on Multi-Path Convolutional Neural Network and fCWT2024 International Conference on Electrical Machines (ICEM)10.1109/ICEM60801.2024.10700419(1-6)Online publication date: 1-Sep-2024
https://doi.org/10.1109/ICEM60801.2024.10700419

Index Terms

Multi Sensor based Bearing Fault Diagnosis of Switched Reluctance Motor for Electric Vehicle
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks

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ICECC '23: Proceedings of the 2023 6th International Conference on Electronics, Communications and Control Engineering

March 2023

316 pages

ISBN:9798400700002

DOI:10.1145/3592307

Copyright © 2023 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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New York, NY, United States

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Published: 14 August 2023

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ICECC 2023

ICECC 2023: 2023 The 6th International Conference on Electronics, Communications and Control Engineering

March 24 - 26, 2023

Fukuoka, Japan

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Cited By

Lorencki JRadkowski S(2024)Mechanical Faults Analysis in Switched Reluctance MotorApplied Sciences10.3390/app1408345214:8(3452)Online publication date: 19-Apr-2024
https://doi.org/10.3390/app14083452
Mohammad-alikhani AMahmouditabar FBaker NNahid-Mobarakeh B(2024)Data-Driven Inter Turn Short Circuit Fault Detection of a Segmented SRM Based on Multi-Path Convolutional Neural Network and fCWT2024 International Conference on Electrical Machines (ICEM)10.1109/ICEM60801.2024.10700419(1-6)Online publication date: 1-Sep-2024
https://doi.org/10.1109/ICEM60801.2024.10700419

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