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

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Khadem Hosseini, Narges, Toshani, Hamid and Abdi, Salman (2023) A Projection-Based Support Vector Machine Algorithm for Induction Motors’ Bearing Fault Detection. In: Proceedings of the 2023 IEEE 14th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives, SDEMPED 2023. International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives, SDEMPED 2023 . The Institute of Electrical and Electronics Engineers (IEEE), GRC, pp. 186-191. ISBN 9798350320770

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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.

Item Type:	Book Section
Uncontrolled Keywords:	bearing fault,feature selection,prnn,svm,signal processing,energy engineering and power technology,computational mechanics,electrical and electronic engineering,mechanical engineering,safety, risk, reliability and quality ,/dk/atira/pure/subjectarea/asjc/1700/1711
Faculty \ School:	Faculty of Science > School of Engineering (former - to 2024)
UEA Research Groups:	Faculty of Science > Research Groups > Sustainable Energy
Related URLs:	http://www.scopus.com/inward/record.url?... https://ieeexplore.ieee.org/abstract/doc...
Depositing User:	LivePure Connector
Date Deposited:	07 Nov 2023 02:55
Last Modified:	07 Nov 2024 12:50
URI:	https://ueaeprints.uea.ac.uk/id/eprint/93583
DOI:	10.1109/SDEMPED54949.2023.10271436

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