Bearing Fault Detection Using Discrete Wavelet Transform and Partitioning Around Medoids Methods

Gigih Priyandoko; Diky Siswanto; Istiadi; Dedy U. Effendi; Eska R. Naufal

doi:10.1007/978-981-33-4597-3_21

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Bearing Fault Detection Using Discrete Wavelet Transform and Partitioning Around Medoids Methods

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Gigih Priyandoko, Diky Siswanto, Istiadi, Dedy U. Effendi, Eska R. Naufal

2022 Lecture Notes in Electrical Engineering Vol. 730 Conference paper Cited by 0

Abstract

Induction motor is widely used in industrial applications. The research paper presents the diagnosis of the induction motor bearing faults using Discrete Wavelet Transforms and Partitioning Around Medoids algorithm methods. The experimental test rig was developed to obtain data of the bearings on healthy or damaged conditions. Several mother-level wavelets are tried in order to get the best performance to find bearing faults. The wavelet transform results are used as an input of the Partitioning Around Medoids algorithm to cluster the bearing condition. The results showed that the methods proposed could provide an accurate diagnosis of the bearing condition. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Affiliations

Department of Electrical Engineering, Faculty of Engineering, University of Widyagama, Malang, Indonesia; Department of Informatics Engineering, Faculty of Engineering, University of Widyagama, Malang, Indonesia

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