Feature extraction of rolling bearing fault signal of: rolling mill based on wavelet packet denoising method

Bingxin Xia; Li Shang; Lei Fan; Dan Wang; Zhihui Xing; Jiping Li

doi:10.1117/12.2623439

1 December 2021 Feature extraction of rolling bearing fault signal of: rolling mill based on wavelet packet denoising method

Bingxin Xia, Li Shang, Lei Fan, Dan Wang, Zhihui Xing, Jiping Li

Author Affiliations +

Proceedings Volume 12079, Second IYSF Academic Symposium on Artificial Intelligence and Computer Engineering; 120792Q (2021) https://doi.org/10.1117/12.2623439
Event: 2nd IYSF Academic Symposium on Artificial Intelligence and Computer Engineering, 2021, Xi'an, China

Abstract

Fault detection and diagnosis of Rolling mill have a very significant role in the practical application. Extracting the bearing fault signal from a multicomponent signal mixture using a variety of signal de-noising method is the first problem to diagnose the rolling mill bearing. Rolling mill bearing belongs to the bearing of low speed and heavy loading. Diagnosis is difficult because of the interference signal and the complex fault mechanism. Wavelet packet de-noising method was applied to extract the fault characteristics of vibration signals. The analysis results show that the wavelet packet analysis method can effectively remove mixed in the interference signal of the rolling mill rolling bearing signal. The characteristics of rolling mill rolling bearing fault signal become more obvious, and the fault diagnosis result becomes more accurate.

Citation Download Citation

Bingxin Xia, Li Shang, Lei Fan, Dan Wang, Zhihui Xing, and Jiping Li "Feature extraction of rolling bearing fault signal of: rolling mill based on wavelet packet denoising method", Proc. SPIE 12079, Second IYSF Academic Symposium on Artificial Intelligence and Computer Engineering, 120792Q (1 December 2021); https://doi.org/10.1117/12.2623439

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