Adaptive Blind Extraction of Rolling Bearing Fault Signal Based on Equivariant Adaptive Separation via Independence

被引：0
|
作者：
Sun J. [1 ]
Zhang W. [1 ]
Lou S. [1 ]
机构：
[1] School of Electronic Engineering, Xidian University, Xi'an
来源：
Dianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology | 2020年 / 42卷 / 10期
基金：
中国国家自然科学基金;
关键词：
Blind signal separation; Fault detection; Fuzzy logic; Sub-Gaussian; Super-Gaussian;
D O I：
10.11999/JEJT18_dzyxxxb-42-10-2471
中图分类号：
学科分类号：
摘要：
For the problem of blind extraction of rolling bearing fault signals under complex working conditions, an adaptive selection method of non-linear functions in Independent Component Analysis (ICA) is proposed, which solves the problem that Equivariant Adaptive Separation via Independence(EASI) can not separate bearing fault signals when multiple vibration sources coexist. In addition, in order to balance the steady-state error and convergence rate of the online blind separation algorithm, an adaptive iterative step selection method based on fuzzy logic is proposed, which improves greatly the convergence speed of the learning algorithm and reduces the steady-state error. The simulation results of blind extraction of bearing fault data verify the performance of the proposed algorithm. © 2020, Science Press. All right reserved.
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收藏
页码：2471 / 2477
页数：6
相关论文
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