自适应分块前向后向分段正交匹配追踪在重构滚动轴承故障信号中应用

doi:10.3901/JME.2020.09.091

机械工程学报 ›› 2020, Vol. 56 ›› Issue (9): 91-101.doi: 10.3901/JME.2020.09.091

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自适应分块前向后向分段正交匹配追踪在重构滚动轴承故障信号中应用

孟宗, 石颖, 潘作舟, 陈子君

燕山大学河北省测试计量技术及仪器重点实验室秦皇岛 066004

收稿日期:2019-05-19 修回日期:2019-12-14 出版日期:2020-05-05 发布日期:2020-05-29
通讯作者: 孟宗(通信作者),男,1977年出生,博士研究生导师。主要研究方向为信号分析与处理,旋转机械故障诊断,压缩感知。E-mail:mzysu@ysu.edu.cn
作者简介:石颖,女,1995年出生,硕士研究生。主要研究方向为压缩感知、信号稀疏分解与稀疏表示。E-mail:1064682611@qq.com;潘作舟,男,1994年出生,博士研究生。主要研究方向为旋转机械故障信号分析与处理,旋转机械寿命预测,压缩感知信号重构。E-mail:792807944@qq.com;陈子君,男,1993年出生,硕士研究生。主要研究方向为滚动轴承故障诊断,压缩感知信号处理。E-mail:1215671354@qq.com
基金资助:
国家自然科学基金（51575472）和河北省自然科学基金（E2019203448）资助项目。

Fault Diagnosis of Rolling Bearing Based on Adaptive Block Forward and Backward Stagewise Orthogonal Matching Pursuit Algorithm

MENG Zong, SHI Ying, PAN Zuozhou, CHEN Zijun

Key Lab of Measurement Technology and Instrumentation of Hebei Province, Yanshan University, Qinhuangdao 066004

Received:2019-05-19 Revised:2019-12-14 Online:2020-05-05 Published:2020-05-29

摘要/Abstract

摘要： 针对滚动轴承故障信号分块压缩感知过程中，因分块之间的稀疏度差异较大以及重构支撑集构造不合理，致使信号重构精度较低，影响信号整体重构效果的问题，提出基于自适应分块前向后向分段正交匹配追踪算法（Adaptive block forward and backward stagewise orthogonal matching pursuit，Adaptive Block-FBStOMP）。首先，依据短时自相关算法确定滚动轴承故障信号自适应分块长度，并根据此长度对信号进行自适应分块；其次，利用K奇异值分解（K-singular value decomposition，K-SVD算法训练稀疏字典；最后，提出FBStOMP算法，在重构过程中增加原子回溯和二次筛选过程，提高有效支撑集原子被全部选入支撑集中的可能性，改善重构效果。通过仿真信号和故障信号试验分析可知，与传统压缩感知重构算法相比，该算法能够有效提升滚动轴承故障信号的重构精度。

关键词: 压缩感知, 自适应分块, 重构算法, 稀疏字典

Abstract: In the process of block compression sensing of rolling bearing fault signal, the reconstruction accuracy of the signal is low due to the large difference in sparsity between blocks and the unreasonable components of reconstruction support set, which affects the overall reconstruction effect of the signal. In order to improve the signal reconstruction results, adaptive block forward and backward stagewise orthogonal matching pursuit (Adaptive Block-FBStOMP) algorithm based on adaptive block method is proposed. Firstly, the adaptive block length of the rolling bearing fault signal is determined according to the short-time autocorrelation algorithm, and the signal is divided into signal blocks according to the adaptive block length to equalize the sparsity of each block. Secondly, the K-singular value decomposition (K-SVD) algorithm is used to train the sparse dictionary of each block to obtain better sparse effect. Finally, the FBStOMP reconstruction algorithm is proposed. The atom backtracking and quadratic screening processes are added in the reconstruction process to improve the possibility that all the effective support set atoms can be selected into the support set. The experimental analysis of the simulation signal and the bearing fault signal shows that, compared with the traditional compressed sensing reconstruction algorithm, the proposing algorithm can effectively improve the reconstruction accuracy of the bearing fault signal.

Key words: compression sensing, adaptive block, reconstruction algorithm, sparse dictionary

中图分类号:

TG156

孟宗, 石颖, 潘作舟, 陈子君. 自适应分块前向后向分段正交匹配追踪在重构滚动轴承故障信号中应用[J]. 机械工程学报, 2020, 56(9): 91-101.

MENG Zong, SHI Ying, PAN Zuozhou, CHEN Zijun. Fault Diagnosis of Rolling Bearing Based on Adaptive Block Forward and Backward Stagewise Orthogonal Matching Pursuit Algorithm[J]. Journal of Mechanical Engineering, 2020, 56(9): 91-101.

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自适应分块前向后向分段正交匹配追踪在重构滚动轴承故障信号中应用

Fault Diagnosis of Rolling Bearing Based on Adaptive Block Forward and Backward Stagewise Orthogonal Matching Pursuit Algorithm

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