广义平滑对数正则化稀疏分解方法研究及其在齿轮箱复合故障诊断中的应用

doi:10.3901/JME.2022.23.123

机械工程学报 ›› 2022, Vol. 58 ›› Issue (23): 123-137.doi: 10.3901/JME.2022.23.123

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广义平滑对数正则化稀疏分解方法研究及其在齿轮箱复合故障诊断中的应用

宋泽树¹, 黄伟国¹, 石娟娟¹, 王俊¹, 沈长青¹, 郭剑峰², 刘金朝², 朱忠奎¹

1. 苏州大学轨道交通学院苏州 215131;
2. 中国铁道科学研究院集团有限公司北京 100081

收稿日期:2022-07-22 修回日期:2022-10-08 出版日期:2022-12-05 发布日期:2023-02-08
通讯作者: 黄伟国(通信作者),男,1981年出生,博士,教授。主要研究方向为机械设备监测与故障诊断,信号特征提取方法。E-mail:wghuang@suda.edu.cn
作者简介:宋泽树,男,1996年出生。主要研究方向为机械设备监测与故障诊断,信号特征提取方法。E-mail:20194246001@stu.suda.edu.cn石娟娟,女,1985年出生,博士,副教授。主要研究方向为机械设备监测与故障诊断,轨道车辆传动系统动力学分析与状态监测。E-mail:wghuang@suda.edu.cn;王俊,男,1987年出生,博士,副教授。主要研究方向为机电系统动态监控,故障诊断与智能维护。E-mail:junking@suda.edu.cn;沈长青,男,1987年出生,博士,副教授。主要研究方向为机械故障诊断与寿命预测。E-mail:cqshen@suda.edu.cn;郭剑峰,男,1987年出生,博士,副研究员。主要研究方向为自适应信号特征提取,机器学习数据建模方法。E-mail:guojf@rails.cn;刘金朝,男,1971年出生,博士,研究员。主要研究方向为车辆振动信号特征提取,轮轨关系建模仿真方法。E-mail:liujinzhao@rails.cn;朱忠奎,男,1974年出生,博士,教授,博士研究生导师。主要研究方向为机械设备故障诊断,车辆系统动力学与控制,测试技术与信号处理。E-mail:zhuzhongkui@suda.edu.cn
基金资助:
国家自然科学基金资助项目(52075353，51875376)。

Research on Generalized Smooth Logarithm Regularization Sparse Decomposition Method and Its Application in Compound Fault Diagnosis of Gearbox

SONG Zeshu¹, HUANG Weiguo¹, SHI Juanjuan¹, WANG Jun¹, SHEN Changqing¹, GUO Jianfeng², LIU Jinzhao², ZHU Zhongkui¹

1. School of Rail Transportation, Soochow University, Suzhou 215131;
2. China Railway Science Research Institute Group Co., Ltd, Beijing 100081

Received:2022-07-22 Revised:2022-10-08 Online:2022-12-05 Published:2023-02-08

摘要/Abstract

摘要： 齿轮箱由于其工况复杂、工作环境恶劣，极易发生故障，并且振动信号中往往包含多种成分并且伴随着强烈的背景噪声，给齿轮箱故障诊断带来了很大的困难。稀疏分解方法能够在强背景噪声下有效地提取瞬态特征成分，针对传统稀疏分解方法存在的计算效率低，幅值低估以及估计精度不足等问题，提出了一种基于调Q小波变换(Tunable Q-factor wavelet transform,TQWT)作为稀疏表示字典的广义平滑对数正则化稀疏分解方法。该方法研究了满足紧框架条件的TQWT来构建稀疏表示字典，然后基于Moreau包络平滑思想提出广义平滑对数正则化方法，该罚函数可以在保持幅值的基础上精确重构出齿轮箱故障瞬态成分，最后利用前向后项分裂(Forward-backward splitting，FBS)算法精确求解该稀疏表示模型。仿真信号和试验信号验证了所提方法在齿轮箱复合故障诊断中的有效性。

关键词: 齿轮箱, 故障诊断, 稀疏分解, 正则化

Abstract: Gearbox is prone to failure due to their complex working conditions and harsh working environment, and the vibration signal often contains multiple components and is accompanied by strong background noise, which brings great difficulties to gearbox fault diagnosis. The sparse decomposition method can effectively extract transient feature components under strong background noise. In view of the problems of traditional sparse decomposition methods that low computational efficiency, underestimation of amplitude, and insufficient estimation accuracy, a generalized smoothing logarithmic regularization sparse decomposition method based on Tunable Q-factor wavelet transform (TQWT) as a sparse representation dictionary is proposed. This method studies the TQWT that satisfies the tight frame condition to construct a sparse representation dictionary, and then proposes a generalized smooth logarithmic regularization method based on the Moreau envelope smoothing idea, which can accurately reconstruct the transient components of the gearbox fault and maintain the amplitude and finally uses the forward-backward splitting (FBS) algorithm to accurately solve the sparse representation model. The simulation signal and the experimental signal verify the effectiveness of the proposed method in the gearbox compound fault diagnosis.

Key words: gearbox, fault diagnosis, sparse decomposition, regularization

中图分类号:

TG156

宋泽树, 黄伟国, 石娟娟, 王俊, 沈长青, 郭剑峰, 刘金朝, 朱忠奎. 广义平滑对数正则化稀疏分解方法研究及其在齿轮箱复合故障诊断中的应用[J]. 机械工程学报, 2022, 58(23): 123-137.

SONG Zeshu, HUANG Weiguo, SHI Juanjuan, WANG Jun, SHEN Changqing, GUO Jianfeng, LIU Jinzhao, ZHU Zhongkui. Research on Generalized Smooth Logarithm Regularization Sparse Decomposition Method and Its Application in Compound Fault Diagnosis of Gearbox[J]. Journal of Mechanical Engineering, 2022, 58(23): 123-137.

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广义平滑对数正则化稀疏分解方法研究及其在齿轮箱复合故障诊断中的应用

Research on Generalized Smooth Logarithm Regularization Sparse Decomposition Method and Its Application in Compound Fault Diagnosis of Gearbox

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