Abstract
The transient shock signal from the vibration signals of rolling bearing has always been a key issue for fault diagnosis. However, due to the influence of other interference components in harsh environment, the transient features are not obvious. Therefore, A diagnosis method based on Laplace wavelet sparse representation and Teager energy operator (TEO) is proposed. Firstly, the observed signal is reconstructed based on the Laplace wavelet sparse representation and basis pursuit to achieve effective noise reduction and extract the transient impact components of bearing faults. Secondly, the TEO is applied to the reconstructed signal to enhance the signal impact characteristics, and the fault characteristic frequency and its multiple frequency are identified through energy spectrum analysis, and finally the rolling bearing fault diagnosis is realized. Through the simulation and experimental verification of rolling bearing vibration signal, the proposed method has better performance of feature extraction and enhanced impact feature.
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The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by Shanxi Scholarship Council of China (Grant No. 2021-050), and Shanxi Provincial Natural Science Foundation (Grant No. 202103021224040).
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Li, H., Mo, R., Wu, Z. (2023). Fault Diagnosis of Rolling Bearing Based on Laplace Wavelet Sparse Representation and Teager Energy Operator. In: Zhang, H., Ji, Y., Liu, T., Sun, X., Ball, A.D. (eds) Proceedings of TEPEN 2022. TEPEN 2022. Mechanisms and Machine Science, vol 129. Springer, Cham. https://doi.org/10.1007/978-3-031-26193-0_95
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DOI: https://doi.org/10.1007/978-3-031-26193-0_95
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