A gear fault feature extraction method based on adaptive AR model and MOMEDA

Long Yong; Fu Qiang

doi:10.1117/12.3025782

1 April 2024 A gear fault feature extraction method based on adaptive AR model and MOMEDA

Long Yong, Fu Qiang

Proceedings Volume 13081, Third International Conference on Advanced Manufacturing Technology and Electronic Information (AMTEI 2023); 130810T (2024) https://doi.org/10.1117/12.3025782
Event: 2023 3rd International Conference on Advanced Manufacturing Technology and Electronic Information (AMTEI 2023), 2023, Tianjin, China

Abstract

Gear fault features are submerged in strong background noise with the influence of background noise and vibration signal transmission paths. The adaptive auto-regressive (AR) model with the largest spectral kurtosis (SK) can effectively eliminate the linear stationary part of the signal, which is selected for signal preprocessing. Multipoint Optimal Minimum Entropy Deconvolution Adjusted (MOMEDA) is suitable for the extraction of periodic fault signal fault features in rotating machinery. Therefore, the adaptive AR and MOMEDA are combined to extract gear fault features, which is verified by experimental results.

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

Citation Download Citation

Long Yong and Fu Qiang "A gear fault feature extraction method based on adaptive AR model and MOMEDA", Proc. SPIE 13081, Third International Conference on Advanced Manufacturing Technology and Electronic Information (AMTEI 2023), 130810T (1 April 2024); https://doi.org/10.1117/12.3025782

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