基于等变化自适应源分离算法的滚动轴承故障信号自适应盲提取  

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

作　　者：孙瑾铃 张伟涛[1] 楼顺天[1] SUN Jinling;ZHANG Weitao;LOU Shuntian(School of Electronic Engineering,Xidian University,Xi’an 710071,China)

机构地区：[1]西安电子科技大学电子工程学院,西安710071

出　　处：《电子与信息学报》2020年第10期2471-2477,共7页Journal of Electronics & Information Technology

基　　金：国家自然科学基金(61571339);陕西省创新人才推进计划-青年科技新星项目(2018KJXX-019)。

摘　　要：针对复杂工况下滚动轴承故障信号盲提取问题,该文提出一种独立分量分析(ICA)中非线性函数自适应选择方法,解决了等变化自适应源分离算法(EASI)在多类振动源共存的情况下无法分离轴承故障信号的问题。此外,为了解决在线盲分离算法稳态误差与收敛速率的平衡问题,提出基于模糊逻辑的自适应迭代步长选择方法,极大地提高了学习算法的收敛速度,且稳态误差更小。轴承故障数据的盲提取仿真结果验证了算法的性能。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.

关 键 词：盲信号分离 故障检测 超高斯 亚高斯 模糊逻辑 

分 类 号：TN911.7[电子电信—通信与信息系统]