Trend Analysis and Continuous Wavelet Transform for Bearing Fault Detection in Induction Motors

Ilker Aydin*, Serhat Seker

*Corresponding author for this work

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

Abstract

In this research, we studied on fault detection at the inner race of bearing in induction motors using trend analysis and continuous wavelet transform. Vibration signals collected from a test setup for normal and faulty cases are analyzed by the trend analysis method. This method aims to determine intersection points of trend lines that represent fault information carrying frequency. Then, the faulty vibration signal is examined with continuous wavelet transform. By using continuous wavelet transform (CWT), it is aimed to calculate the frequency resulting the highest wavelet coefficients which describe the fault signal frequency.

Original languageEnglish
Title of host publication14th International Conference on Electrical and Electronics Engineering, ELECO 2023 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350360493
DOIs
Publication statusPublished - 2023
Externally publishedYes
Event14th International Conference on Electrical and Electronics Engineering, ELECO 2023 - Virtual, Bursa, Turkey
Duration: 30 Nov 20232 Dec 2023

Publication series

Name14th International Conference on Electrical and Electronics Engineering, ELECO 2023 - Proceedings

Conference

Conference14th International Conference on Electrical and Electronics Engineering, ELECO 2023
Country/TerritoryTurkey
CityVirtual, Bursa
Period30/11/232/12/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

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