Bearing Fault Diagnosis in Induction Motor Using Hybrid CNN Model

Prashant Kumar, Ananda Shankar Hati, Prince, Heung Soo Kim

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Induction motors (IMs) are the prime movers for the industries. The availability of an efficient electrical drive has aided in the widespread application of IMs in different sectors, including mining, cement, textile, and many more. Bearings are the critical components of the motors. The bearing failure may cause severe accidents and production losses. The timely detection of the bearing fault is essential for the minimum downtime. Researchers have used conventional machine learning techniques for the bearing fault detection in motors. However, these approaches require input features, and selecting efficient features poses a big challenge. Deep learning (DL) algorithms have recently captured the interest of researchers all over the world. DL algorithms like convolutional neural networks (CNNs) can automatically execute feature extraction and selection. This paper proposes a hybrid CNN-based model in combination with support vector machine for bearing fault detection in IMs. Various bearing faults, such as inner race fault, outer race fault, and ball defect, have been considered in the proposed work. The proposed method has efficiently detected various bearing faults. The proposed approach has achieved a mean accuracy of more than 99%. Python was used for all of the analysis and programming.

Original languageEnglish
Title of host publicationRecent Advances in Industrial Machines and Mechanisms - Select Proceedings of IPRoMM 2022
EditorsSanjoy K. Ghoshal, Arun K. Samantaray, Sandipan Bandyopadhyay
PublisherSpringer Science and Business Media Deutschland GmbH
Pages411-418
Number of pages8
ISBN (Print)9789819942695
DOIs
StatePublished - 2024
Event2nd International and 14th National Conference on Industrial Problems on Machines and Mechanisms, IPRoMM 2022 - Dhanbad, India
Duration: 22 Dec 202223 Dec 2022

Publication series

NameLecture Notes in Mechanical Engineering
ISSN (Print)2195-4356
ISSN (Electronic)2195-4364

Conference

Conference2nd International and 14th National Conference on Industrial Problems on Machines and Mechanisms, IPRoMM 2022
Country/TerritoryIndia
CityDhanbad
Period22/12/2223/12/22

Fingerprint

Dive into the research topics of 'Bearing Fault Diagnosis in Induction Motor Using Hybrid CNN Model'. Together they form a unique fingerprint.

Cite this