Affiliation:
1. School of Aerospace Engineering, Xiamen University, Xiamen 361102, China
2. National Center for Applied Mathematics, Chongqing Normal University, Chongqing 401331, China
Abstract
Vibration monitoring is one of the most effective approaches for bearing fault diagnosis. Within this category of techniques, sparsity constraint-based regularization has received considerable attention for its capability to accurately extract repetitive transients from noisy vibration signals. The optimal solution of a sparse regularization problem is determined by the regularization term and the data fitting term in the cost function according to their weights, so a tradeoff between sparsity and data fidelity has to be made inevitably, which restricts conventional regularization methods from maintaining strong sparsity-promoting capability and high fitting accuracy at the same time. To address the limitation, a stepwise sparse regularization (SSR) method with an adaptive sparse dictionary is proposed. In this method, the bearing fault diagnosis is modeled as a multi-parameter optimization problem, including time indexes of the sparse dictionary and sparse coefficients. Firstly, sparsity-enhanced optimization is conducted by amplifying the regularization parameter, making the time indexes and the number of atoms adaptively converge to the moments when impulses occur and the number of impulses, respectively. Then, fidelity-enhanced optimization is carried out by removing the regularization term, thereby obtaining the high-precision reconstruction amplitudes. Simulations and experiments verify that the reconstruction accuracy of the SSR method outperforms other sparse regularization methods under most noise conditions, and thus the proposed method can provide more accurate results for bearing fault diagnosis.
Funder
Foundation of Chongqing Normal University
Reference36 articles.
1. Rolling element bearing diagnostics—A tutorial;Randall;Mech. Syst. Signal. Process.,2011
2. Gradzki, R., Bartoszewicz, B., and Martinez, J.E. (2023). Bearing Fault Diagnostics Based on the Square of the Amplitude Gains Method. Appl. Sci., 13.
3. Fault Diagnosis Method of Low-Speed Rolling Bearing Based on Acoustic Emission Signal and Subspace Embedded Feature Distribution Alignment;Chen;IEEE Trans. Ind. Inform.,2021
4. Acoustic Emission Analysis for Wind Turbine Blade Bearing Fault Detection Under Time-Varying Low-Speed and Heavy Blade Load Conditions;Liu;IEEE Trans. Ind. Appl.,2021
5. Qiu, Y., Tan, B., Li, D., Jiang, H., and Feng, Y. (2015, January 17–18). Thermal analysis of rolling bearing at wind turbine gearbox high speed end. Proceedings of the International Conference on Renewable Power Generation (RPG 2015), Beijing, China.
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献