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Article Details

Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

Hybrid Fault Diagnosis Method based on Wavelet Packet Energy Spectrum and SSA-SVM

Author 1: Jinglei Qu
Author 2: Bingxin Ma
Author 3: Xiaojie Ma
Author 4: Mengmeng Wang

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Digital Object Identifier (DOI) : 10.14569/IJACSA.2022.0130508

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 13 Issue 5, 2022.

  • Abstract and Keywords
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Abstract: As one of the important components of mechanical equipment, rolling bearing has been widely used, and its motion state affects the safety and performance of equipment. To enhance the fault feature information in the bearing signal and improve the classification accuracy of support vector machine, a hybrid fault diagnosis method based on wavelet packet energy spectrum and SSA-SVM is proposed. Firstly, the wavelet packet decomposition is used to decompose vibration signals to generate frequency band energy spectrum, and the bearing characteristic information is constructed from the energy spectrum to extract and enhance the bearing fault characteristic information. Secondly, the penalty and kernel parameters are optimized globally by sparrow search algorithm to improve the classification accuracy of support vector machine, and then construct the WPES-SSA-SVM model. Finally, the proposed model is used to diagnose and analyze the measured signals. Compared with BP, ELM and SVM, the effectiveness and superiority of the proposed method are verified.

Keywords: Wavelet packet energy spectrum; sparrow search optimization; support vector machine; rolling bearing

Jinglei Qu, Bingxin Ma, Xiaojie Ma and Mengmeng Wang, “Hybrid Fault Diagnosis Method based on Wavelet Packet Energy Spectrum and SSA-SVM” International Journal of Advanced Computer Science and Applications(IJACSA), 13(5), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130508

@article{Qu2022,
title = {Hybrid Fault Diagnosis Method based on Wavelet Packet Energy Spectrum and SSA-SVM},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130508},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130508},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {5},
author = {Jinglei Qu and Bingxin Ma and Xiaojie Ma and Mengmeng Wang}
}


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