非线性稀疏盲解卷积的轴承早期故障诊断方法

doi:10.3901/JME.2023.16.157

机械工程学报 ›› 2023, Vol. 59 ›› Issue (16): 157-166.doi: 10.3901/JME.2023.16.157

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非线性稀疏盲解卷积的轴承早期故障诊断方法

张宗振^1,2, 王金瑞¹, 韩宝坤¹, 鲍怀谦¹, 李舜酩²

1. 山东科技大学机械电子工程学院青岛 266590;
2. 南京航空航天大学能源与动力学院南京 210016

收稿日期:2022-07-05 修回日期:2022-10-01 出版日期:2023-08-20 发布日期:2023-11-15
通讯作者: 王金瑞(通信作者),男,1989年出生,博士,教授。主要研究方向为机械智能故障诊断与数字孪生。E-mail:wangjr33@163.com
作者简介:张宗振,男,1986年出生,博士,副教授。主要研究方向为机械装备早期智能故障诊断与剩余寿命预测。E-mail:zhzz18@126.com
基金资助:
国家自然科学基金(52105110,52005303,51975276);国家重点研发计划(2018YFB2003300);山东省自然科学基金(ZR202020QE157,ZR2021QE024,ZR2022ME119)资助项目。

Early Stage Fault Diagnosis Method of Bearings Based on Nonlinear Sparse Blind Deconvolution

ZHANG Zongzhen^1,2, WANG Jinrui¹, HAN Baokun¹, BAO Huaiqian¹, LI Shunming²

1. College of Mechanical and Electronic Engineering, Shandong University of Science and Technology, Qingdao 266590;
2. College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016

Received:2022-07-05 Revised:2022-10-01 Online:2023-08-20 Published:2023-11-15

摘要/Abstract

摘要： 针对大数据环境下旋转机械的早期故障诊断,提出一种基于非线性稀疏盲解卷积的快速高效的轴承故障诊断方法。首先对无监督学习目标函数的故障表达能力进行了理论研究,结果表明,适当的非线性伸缩能够改善目标函数的相对梯度,提高异常干扰下故障表达的稳定性;然后利用归一化后的广义非线性卷积激活解决了非线性函数的尺度不一致导致的分布异变问题,并构造非线性稀疏盲解卷积目标函数,搭建无监督神经网络;为提高滤波器的冲击特征,通过自适应拟合的高斯窗函数对学习到的滤波器进行修正,并通过频域峭度对滤波器组进行降维;最后进行滤波和包络分析,得到轴承的早期故障特征。通过仿真外圈故障、远端内圈故障和轴承加速寿命数据进行验证和对比。结果表明,非线性稀疏盲解卷积能够自主学习并增强微弱的早期故障,具备更强的噪声适应性、计算时间和鲁棒性,为实现机械装备的实时在线监测提供良好的理论支撑,展现出良好的应用前景。

关键词: 无监督学习, 稀疏盲解卷积, 早期故障诊断, 旋转机械

Abstract: Aiming at the early fault diagnosis of rotating machinery under big data environment, a fast and efficient method under strong interference based on nonlinear sparse blind deconvolution is proposed. Firstly, the fault expression ability of the objective function of unsupervised learning is studied theoretically. The results show that appropriate nonlinear scaling can improve the relative gradient and the stability of fault expression under abnormal impulsive interference. Then, the normalization and generalized nonlinear convolution activation are used to solve the distribution variation caused by the inconsistent scaling of the nonlinear function. The objective function of the nonlinear sparse blind deconvolution is constructed to build an unsupervised neural network.Adaptive fitting Gaussian window function is used to improve the impulse characteristics of the filters. The final filter is selected using frequency domain kurtosis. Finally, the early fault characteristics of the bearing are obtained by filtering and envelope analysis.The proposed method is verified and compared through the simulation of outer ring fault, distal inner ring fault and IMS test-to-failure bearing data. The results show that the proposed method can learn and enhance weak early faults automatically, and has superior noise adaptability, computational time and robustness. This study provides a good theoretical support for the realization of real-time online monitoring of mechanical equipment and shows a good application prospect.

Key words: unsupervised learning, sparse blind deconvolution, early fault diagnosis, rotating machinery

中图分类号:

TG156

张宗振, 王金瑞, 韩宝坤, 鲍怀谦, 李舜酩. 非线性稀疏盲解卷积的轴承早期故障诊断方法[J]. 机械工程学报, 2023, 59(16): 157-166.

ZHANG Zongzhen, WANG Jinrui, HAN Baokun, BAO Huaiqian, LI Shunming. Early Stage Fault Diagnosis Method of Bearings Based on Nonlinear Sparse Blind Deconvolution[J]. Journal of Mechanical Engineering, 2023, 59(16): 157-166.

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非线性稀疏盲解卷积的轴承早期故障诊断方法

Early Stage Fault Diagnosis Method of Bearings Based on Nonlinear Sparse Blind Deconvolution

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