@inproceedings{5b3579c9df7142559a67b0ca1618d8da,
title = "Within and among Wavelet-subband Sparse Decomposition for Bearing Fault Diagnosis",
abstract = "Extracting fault components from structural noise is one of the key challenges in bearing fault diagnosis. In this paper, we research the sparsity of bearing fault signals in the wavelet domain and propose Within and Among Wavelet-subband Sparse Decomposition (WAWSD) for extracting the fault signal. Based on the sparse distribution of fault signals in the wavelet domain, two constraints based on the sparsity within and among wavelet-subband prior are proposed respectively. Then, an optimization algorithm based on the thought of Iterative Atomic Decomposition Thresholding (IADT) is proposed to solve the proposed highly non-convex model. The performance of the WAWSD is verified by the simulation study and the run-to-failure experiment.",
keywords = "Sparse prior model, fault diagnosis, non-convex",
author = "Haiwei Ren and Baoqing Ding and Lei Jin and Zhibin Zhao and Chuang Sun and Xingwu Zhang and Ruqiang Yan and Xuefeng Chen",
note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 2025 IEEE International Instrumentation and Measurement Technology Conference, I2MTC 2025 ; Conference date: 19-05-2025 Through 22-05-2025",
year = "2025",
doi = "10.1109/I2MTC62753.2025.11079077",
language = "英语",
series = "Conference Record - IEEE Instrumentation and Measurement Technology Conference",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "IEEE International Instrumentation and Measurement Technology Conference, I2MTC 2025 - Proceedings",
}