组稀疏低秩矩阵估计的变转速滚动轴承故障特征提取

Ran Wang, Junwu Zhang, Liang Yu

科研成果: 期刊稿件文章同行评审

摘要

Effective extraction of early bearing failure features is of great importance to avoid serious mechanical accidents. The impulse signals characterizing bearing faults are often submerged in strong background noise interference, and the bearings often operate under variable speed conditions, which makes the task of fault feature extraction more difficult. To address this issue, one kind of group sparse low-rank matrix estimation algorithm for rolling bearing fault feature extraction under variable speed conditions was proposed in this paper. Firstly, the measured signal was transformed into the order-frequency domain by using order-frequency spectral correlation (OFSC) according to the angle/time cyclist-ationarity of the bearing fault pulse signal under variable speed conditions. Secondly, the group sparsity and low-rank property of the bearing fault pulse in the order-frequency domain were revealed, and a convex optimization problem was constructed to enhance these two properties accordingly, and a non convex penalty function was introduced to improve the sparsity of the fault characteristics. Again, the convex optimization problem was solved in the framework of the alternating direction method of multipliers (ADMM) and majorization-minimization (MM), and a group sparse low-rank (GSLR) matrix estimation algorithm was derived. Finally, the target components obtained from the solution were detected by constructing the enhanced envelope order spectrum (EEOS) for the fault features. The analysis of simulation and experimental signals verify the effectiveness of the method in fault feature extraction.

投稿的翻译标题Group sparse low-rank matrix estimation for variable speed rolling bearing fault feature extraction
源语言繁体中文
页(从-至)92-100+119
期刊Zhendong yu Chongji/Journal of Vibration and Shock
42
16
DOI
出版状态已出版 - 2023
已对外发布

关键词

  • enhanced envelope order spectrum (EEOS)
  • feature extraction
  • group sparse low-rank (GSLR)
  • nonconvex penalty function
  • variable speed condition

指纹

探究 '组稀疏低秩矩阵估计的变转速滚动轴承故障特征提取' 的科研主题。它们共同构成独一无二的指纹。

引用此