Bearing fault diagnosis based on improved maximum correlation kurtosis deconvolution and improved empirical wavelet transform signal processing

Maidina Bahati; Maimaitireyimu Abulizi

doi:10.1117/12.3029294

6 May 2024 Bearing fault diagnosis based on improved maximum correlation kurtosis deconvolution and improved empirical wavelet transform signal processing

Maidina Bahati, Maimaitireyimu Abulizi

Author Affiliations +

Proceedings Volume 13107, Fourth International Conference on Sensors and Information Technology (ICSI 2024); 1310725 (2024) https://doi.org/10.1117/12.3029294
Event: Fourth International Conference on Sensors and Information Technology (ICSI 2024), 2024, Xiamen, China

Abstract

There are a lot of noise signals for the early fault signals of rolling bearings, the fault feature extraction is inconvenient and the signal feature is weak and difficult to extract, process the extracted complex information using improved and optimized maximum correlation kurtosis deconvolution. The filter length L is optimized by grid search method with energy entropy as the standard function; The number of convolution periods T conforming to the characteristics of each fault is calculated by the empirical formula; The parameter optimization of shift number M is determined by experimental iteration. The improved empirical wavelet transform is applied to the filtered signal, and the adaptive selection of the number of modal components is proposed for feature extraction and fault analysis. Experiments show that the combination of parameter optimization maximum correlation kurtosis deconvolution method and improved empirical wavelet transform method can effectively analyze display the frequency and determine the fault status based on it.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Maidina Bahati and Maimaitireyimu Abulizi "Bearing fault diagnosis based on improved maximum correlation kurtosis deconvolution and improved empirical wavelet transform signal processing", Proc. SPIE 13107, Fourth International Conference on Sensors and Information Technology (ICSI 2024), 1310725 (6 May 2024); https://doi.org/10.1117/12.3029294

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