Abstract
To address challenges in fault diagnosis of rolling bearing caused by great noise contamination and difficult extraction of fault character frequency, a fault diagnosis method of rolling bearing based on the maximum correlation kurtosis deconvolution (MCKD), singular spectral decomposition (SSD) and teager energy operator (TEO) with optimal parameters was proposed in this study. First of all, denoising was performed as a preprocessing to the original vibration signals which were collected by using the MCKD with optimal parameters to highlight the impact component. Next, SSD was performed to the preprocessed signals and the optimal components were selected according to variance contribution. Finally, the energy spectra of optimal components were calculated and characteristic frequency was extracted to realize fault diagnosis of bearing. Through simulation and experimental analysis, the proposed method was proved feasible. It was further compared with empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD), which proved superiority and validity of the proposed method.
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Acknowledgments
This work was supported by the National Natural Science Foundation of China (51769007), Yunnan Provincial Local Undergraduate College Basic Research Joint Special Key Project (202001BA070001-002), and Xingdian Talents Support Program (YNWR-QNBJ-2018-349).
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Ben Cui studied at Kunming University of Science and Technology, master’s degree. His main research direction is fault diagnosis of rotating machinery.
Wenbin Zhang obtained a doctorate degree from Zhejiang University, Professor. His main research interests are pattern recognition and intelligent diagnosis.
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Cui, B., Guo, P. & Zhang, W. Research on fault diagnosis of rolling bearing based on the MCKD-SSD-TEO with optimal parameters. J Mech Sci Technol 37, 31–42 (2023). https://doi.org/10.1007/s12206-022-1205-4
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DOI: https://doi.org/10.1007/s12206-022-1205-4