期刊
ISA TRANSACTIONS
卷 101, 期 -, 页码 366-378出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.isatra.2020.01.037
关键词
Periodical sparse low-rank (PSLR) matrix estimation; Convex optimization problem; Gini-guided fault information thresholding (FIT) scheme; Fault detection; Rolling bearings
资金
- National Natural Science Foundation of China [51575424]
- Joint Foundation of the Ministry of Education, China [6141A02022113]
- National Science and Technology Major Project, China [2014ZX04001191]
Early bearing fault detection is crucial to avoid catastrophic accidents. However, the repetitive defect impulses indicating bearing fault are buried in heavy background noise. In the paper, a novel periodical sparse low-rank (PSLR) matrix estimation algorithm is proposed for extracting repetitive transients from noisy signal. Concretely, periodical group sparsity and low-lank property of fault transients in time-frequency domain are first revealed, and then an optimization problem is proposed for simultaneously promoting these two properties. Meanwhile, to further highlight the sparsity of fault features, the non-convex penalty functions are incorporated into the optimization problem. Then, for solving the proposed optimization problem, an iterative algorithm is derived based on alternating direction method of multipliers (ADMM) and majorization-minimization (MM), in which the traditional soft-thresholding operation is replaced by the proposed Gini-guided fault information thresholding (FIT) scheme to enhance fault transient extraction. Finally, simulated and real signals confirm the performance of proposed PSLR in extracting defect impulses from noisy vibration signal. (c) 2020 ISA. Published by Elsevier Ltd. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据