深度子领域自适应网络电机滚动轴承跨工况故障诊断

doi:10.19595/j.cnki.1000-6753.tces.221854

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摘要

针对实际生产中旋转机械工况变化引起状态监测数据分布差异及获取待诊断样本标签困难问题,提出多尺度子域自适应模型（MSDAM）的跨工况下滚动轴承故障诊断方法。首先,以原始振动信号作为输入,无需信号预处理及人工特征参数提取;其次,搭建多尺度卷积神经网络将已知标签样本和待诊断样本特征迁移到同一子空间,捕获具有细粒度信息的多尺度公共特征;然后,以不同的故障类型来划分相关子域,并通过局部最大均值距离（LMMD）来完成子域的适配,有效削弱不同工况同类故障特征分布差异;最后,在三个数据集的多个迁移任务上进行试验验证,结果证明,所提MSDAM的跨工况故障诊断性能优于关注全局领域适配的迁移学习方法。

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	宋向金
	孙文举
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	赵文祥
	王照伟

关键词 ：轴承故障诊断, 子领域自适应, 迁移学习, 软标签学习

Abstract：

In practical industrial situations, the variations in the working conditions cause the vibration data gathered from mechanical equipment to present different probability distributions. Moreover, almost all of the gathered vibration data is actually unlabeled. Therefore, the unlabeled vibration data may be misclassified by the diagnostic models trained by sufficient labeled data in source domains. Deep transfer learning with the marginal distribution or the conditional distribution adaptationcan extend the knowledge of the trained models in source domains to the novel but related target domains. However, the only reduction of marginal distribution discrepancylimits the generalization ability and performance of the diagnostic model. Besides, the most effective diagnostic model that matches both marginal and conditional distributions fails to take full advantage of complex and rich sensitive features in the data set. To address these issues, a multiscale subdomain adaptive model (MSDAM) for rolling bearings fault diagnosis under cross working conditions is proposed.
Firstly, in order to eliminate signal preprocessing and manual feature extraction dependencies, the original vibration signal is used as the input of the proposed MSDAM. Secondly, a multiscale convolutional neural network is built to capture multiscale common features with fine-grained information and transfer the features of labeled source data and unlabeled target data to the same subspace. Then, the prediction results of the source classifier are used as the soft labels for the unlabeled target data, and the relevant subdomains are divided according to different fault types. Finally,the local maximum mean distance (LMMD) method is employed to align the conditional distributions of the subdomains in the common feature space to effectively reduce the distribution discrepancy of similar fault characteristics under different working conditions.
The location selection experiment of the subdomain adaptation layer shows that the subdomain adaptation layer is set before the classification layer, which can better guide the optimization of the entire network and maintain feature distribution matching. The results on the Paderborn bearing dataset show that the average diagnostic accuracy of the multiscale model is improved by 7.98% compared to the single-scale model. Despite sacrificing extra training time, the proposed MSDAM has stronger transfer ability. To further verify the effectiveness of the proposed MSDAM, a total of 12 transfer learning tasks are carried out on the CWRU bearing dataset, Paderborn bearing dataset, and laboratory built datasets respectively. The experimental results indicate that the proposed MSDAM has higher accuracy in fault diagnosis across working conditions than the deep adaptation methods such as domain adaptive networks(DAN) anddomain-adversarial neural networks (DANN). In addition, t-SNE visualization of the subdomain adaptation layer representations again indicates that the proposed MSDAM can accurately align the relevant subdomain distributions.
The following conclusions can be drawn through the analysis of experimental results: (1) The proposed MSDAM can extract more fine-grained common fault features and align relevant subdomains based on LMMD compared with DAN and DANN. (2) The proposed MSDAM only requires the original vibration signal as input, eliminating the need for signal preprocessing and manual feature extraction. (3) A high cross domain diagnostic accuracy on all three datasets proves the strong generalization ability of the proposed MSDAM.

Key words： Bearing fault diagnosis subdomain adaptation transfer learning soft label learning

收稿日期: 2022-09-30

PACS:

TM307

基金资助:

国家自然科学基金(青年科学基金项目)(52007078和62002140); 江苏省自然科学基金资助项目(BK20200887); 江苏省基础研究计划青年基金项目（BK20200887）; 江苏省“双创”项目

通讯作者: 宋向金, 女,1989年生,博士,讲师,硕士研究生导师,研究方向为交流电机状态监测与故障诊断。E-mail：songxiangjin@ujs.edu.cn

作者简介: 孙文举, 男,1998年生,硕士研究生,研究方向为旋转机械设备故障诊断。E-mail：2222107041@stmail.ujs.edu.cn

引用本文:

宋向金, 孙文举, 刘国海, 赵文祥, 王照伟. 深度子领域自适应网络电机滚动轴承跨工况故障诊断[J]. 电工技术学报, 0, (): 8904-. Song Xiangjin, Sun Wenju, Liu Guohai, Zhao Wenxiang, Wang zhaowei. Across Working Conditions Fault Diagnosis for MotorRolling Bearing based on Deep Subdomain Adaption Network. Transactions of China Electrotechnical Society, 0, (): 8904-.

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