A Projection-Based Support Vector Machine Algorithm for Induction Motors’ Bearing Fault Detection

Narges Khadem Hosseini, Hamid Toshani, Salman Abdi

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Abstract

This paper proposes a binary fault detection algorithm for detecting inner raceway bearing faults in a 4KW induction motor. The algorithm uses Support Vector Machine (SVM) and Projection Recurrent Neural Network (PRNN) techniques and is based on data collected experimentally at different speeds and load conditions. Time and frequency contents of the three-phase stator currents are analysed using Discrete Wavelet Transform (DWT), Power Spectral Density (PSD), and cepstrum analysis. A feature set is obtained using various statistical measures, and feature selection algorithms are used to select the most relevant features. The SVM is then trained using these features, and its optimisation problem is formulated as Constrained Nonlinear Programming (NCP). A PRNN is proposed to solve the NCP and obtain the optimal decision boundary of the SVM. The study demonstrates that the accuracy of the algorithm depends on the type of kernel function and the number of relevant features selected. The results suggest that the proposed algorithm is effective in detecting inner raceway bearing faults in induction motors.
Original languageEnglish
Title of host publicationProceedings of the 2023 IEEE 14th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives, SDEMPED 2023
EditorsLuca Zarri, Sang Bin Lee
PublisherThe Institute of Electrical and Electronics Engineers (IEEE)
Pages186-191
Number of pages6
ISBN (Electronic)9798350320770
DOIs
Publication statusPublished - 9 Oct 2023
Event2023 IEEE 14th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED) - Chania, Greece
Duration: 28 Aug 202331 Aug 2023

Publication series

NameInternational Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives, SDEMPED 2023

Conference

Conference2023 IEEE 14th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED)
Country/TerritoryGreece
CityChania
Period28/08/2331/08/23

Keywords

  • Bearing fault
  • Feature selection
  • PRNN
  • SVM

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