基于分量加权重构和稀疏NMF的轮毂电机轴承复合故障特征提取方法

doi:10.3901/JME.2023.09.146

机械工程学报 ›› 2023, Vol. 59 ›› Issue (9): 146-156.doi: 10.3901/JME.2023.09.146

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基于分量加权重构和稀疏NMF的轮毂电机轴承复合故障特征提取方法

薛红涛^1,2, 丁殿勇¹, 李汭铖¹, 徐兴³

1. 江苏大学汽车与交通工程学院镇江 212013;
2. 江苏大学振动噪声研究所镇江 212013;
3. 江苏大学汽车工程研究院镇江 212013

收稿日期:2022-05-20 修回日期:2022-10-08 出版日期:2023-05-05 发布日期:2023-07-19
通讯作者: 薛红涛(通信作者),男,1978年出生,博士,副教授,博士研究生导师。主要研究方向为智能网联汽车安全和故障诊断自动化技术、信号处理和特征提取、人工智能及状态识别。E-mail:xueht@ujs.edu.cn E-mail:xueht@ujs.edu.cn
作者简介:丁殿勇,男,1998年出生,硕士研究生。主要研究方向为信号处理、特征提取和故障诊断自动化技术。E-mail:ddyujs@foxmail.com李汭铖,男,1997年出生,硕士研究生。主要研究方向为信号处理、特征提取和汽车安全技术。E-mail:609373768@qq.com徐兴,男,1979年出生,博士,教授,博士研究生导师。主要研究方向为车辆动力系控制、电动化底盘集成设计、自动驾驶控制。E-mail:xuxing@ujs.edu.cn
基金资助:
国家自然科学基金资助项目(51775245)。

Feature Extraction Method Based on Component Weighted Reconstruction and Sparse NMF for Bearing Compound Faults of In-wheel Motor

XUE Hongtao^1,2, DING Dianyong¹, LI Ruicheng¹, XU Xing³

1. School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013;
2. Institute of Vibration and Noise, Jiangsu University, Zhenjiang 212013;
3. Automotive Engineering Research Institute, Jiangsu University, Zhenjiang 212013

Received:2022-05-20 Revised:2022-10-08 Online:2023-05-05 Published:2023-07-19

摘要/Abstract

摘要： 为解决复合故障特征分离提取效果不佳的问题,针对轮毂电机轴承故障,提出一种基于分量加权重构(Component weighted reconstruction, CWR)与稀疏非负矩阵分解(Sparse nonnegative matrix factorization, SNMF)相结合的故障特征提取方法。首先,提出了一种融合指标CIH,从多角度评价振动信号并自适应选取局部均值分解后的乘积函数(Product function, PF)分量,进行CWR实现故障特征的增强表达;其次,分析重构信号时频能量矩阵的奇异值与隐含子空间的关系,基于奇异值方差比定义重构信号的平滑系数,对SNMF算法最优分解维数进行预估计;最后,引入板仓-斋藤(Itakura-Saito, IS)距离和稀疏约束建立SNMF算法,对时频能量矩阵进行分解降维,通过短时傅里叶逆变换获得子空间时域分离信号并进行频谱包络分析,提取故障特征。由仿真及试验结果表明,所提方法可以有效实现复合故障特征的分离提取,具有一定的工程应用价值。

关键词: 轮毂电机, 复合故障, 特征提取, 分量加权重构, 稀疏非负矩阵分解

Abstract: To solve the problem of poor separation and extraction for compound fault features, a fault feature extraction method based on component weighted reconstruction (CWR) and sparse non-negative matrix factorization (SNMF) is proposed for the fault of in-wheel motor bearings. Firstly, a fusion index CIH is proposed to evaluate the information of the vibration signal from multiple perspectives and adaptively select the product function (PF) component by the local mean decomposition, then perform CWR to enhance the expression of fault characteristics. Secondly, the relationship between singular values and implicit subspace of the time-frequency energy matrix of the constructed signal is analysed, and the smoothing coefficient of the constructed signal is defined based on the variance ratio of the singular values, which is used to estimate the optimal decomposition dimension of the SNMF algorithm. Finally, Itakura-Saito (IS) distance and sparse constraint are employed to establish a SNMF algorithm, which is used to decompose the time-frequency energy matrix for dimension reduction. The subspace time-domain separation signals are obtained by the inverse short-time Fourier transform, and the fault features are extracted by spectral envelope analysis. Simulation and experiment results prove that the proposed method has realized effectively the separation and extraction of compound fault features, and has certain value in engineering application.

Key words: in-wheel motor, compound fault, feature extraction, component weighted reconstruction, sparse non-negative matrix factorization

中图分类号:

TG156

薛红涛, 丁殿勇, 李汭铖, 徐兴. 基于分量加权重构和稀疏NMF的轮毂电机轴承复合故障特征提取方法[J]. 机械工程学报, 2023, 59(9): 146-156.

XUE Hongtao, DING Dianyong, LI Ruicheng, XU Xing. Feature Extraction Method Based on Component Weighted Reconstruction and Sparse NMF for Bearing Compound Faults of In-wheel Motor[J]. Journal of Mechanical Engineering, 2023, 59(9): 146-156.

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基于分量加权重构和稀疏NMF的轮毂电机轴承复合故障特征提取方法

Feature Extraction Method Based on Component Weighted Reconstruction and Sparse NMF for Bearing Compound Faults of In-wheel Motor

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