An improved adaptive minimum entropy deconvolution method in bearing fault detection

Zihao Liao; Jinglun Xu; Minglv Jin; Yuqi Cao; Yunqi Cao; Dibo Hou; Pingjie Huang

doi:10.1117/12.3030623

5 June 2024 An improved adaptive minimum entropy deconvolution method in bearing fault detection

Zihao Liao, Jinglun Xu, Minglv Jin, Yuqi Cao, Yunqi Cao, Dibo Hou, Pingjie Huang

Proceedings Volume 13163, Fourth International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024); 131637I (2024) https://doi.org/10.1117/12.3030623
Event: International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024), 2024, Xi'an, China

Abstract

Bearings, a crucial element in rotating machinery, are most susceptible to failure during operation, making up over half of all malfunctions. Detecting bearing faults in a timely manner can be quite challenging due to the frequent influence of strong noise on the fault features. To address the issue, an adaptive parameter optimization based on Minimum Entropy Deconvolution (MED) method in conjunction with Infogram is proposed. First, the Infogram is created through spectral negentropy, followed by the construction of a filter that utilizes the center frequency and the frequency band exhibiting the highest spectral negentropy. This filter is then applied to the original vibration signal in order to isolate the frequency band that contains the most significant bearing fault information. Then, based on MED method, the autocorrelation function and L1 norm are introduced to evaluate the filtered signal to realize the adaptive search of deconvolution filter length. Finally, the characteristic frequency components are extracted by envelope spectrum analysis. The validation of the effectiveness of the proposed method is conducted on a public dataset across various bearing operating conditions. The verification results show that this method can accurately detect the conditions of bearings, and extract more distinctive characteristic frequency components compared with some relevant techniques.

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

Citation Download Citation

Zihao Liao, Jinglun Xu, Minglv Jin, Yuqi Cao, Yunqi Cao, Dibo Hou, and Pingjie Huang "An improved adaptive minimum entropy deconvolution method in bearing fault detection", Proc. SPIE 13163, Fourth International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024), 131637I (5 June 2024); https://doi.org/10.1117/12.3030623

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