基于角域重采样和领域对抗网络的滚动轴承故障迁移诊断方法及实验分析  

Fault transfer diagnosis method and experimental analysis of rolling bearings based on angular domain resampling and domain-adversarial networks

作　　者：王攀攀[1] 李兴宇 戴诗科 徐瑞东[1] 王宇佩 陈凯玄 邓先明[1] WANG Panpan;LI Xingyu;DAI Shike;XU Ruidong;WANG Yupei;CHEN Kaixuan;DENG Xianming(School of Electrical Engineering,China University of Mining and Technology,Xuzhou 221116,China;Xiangshan Power Supply Company,State Grid Zhejiang Electric Power Co.,Ltd,Ningbo 315700,China)

机构地区：[1]中国矿业大学电气工程学院,江苏徐州221116 [2]国网浙江省电力有限公司象山县供电公司,浙江宁波315700

出　　处：《实验技术与管理》2024年第5期54-61,共8页Experimental Technology and Management

基　　金：2023江苏省高等教育教改研究立项课题(2023JSJG345);2022年中国矿业大学自制实验教学设备重点项目(SZZ2022Z005);2022年中国矿业大学“教育数字化”专项教学研究课题(2022ZX10)。

摘　　要：该文提出一种基于角域重采样和领域对抗神经网络的电机滚动轴承跨工况故障迁移诊断方法。首先对不同工况下的时域振动信号进行角域重采样,用以降低不同转速下振动信号的时频差异;然后利用领域对抗学习策略提取出源域与目标域数据中的领域不变特征,进一步减小不同工况间的数据分布差异。该文还搭建了电机滚动轴承故障诊断实验平台,针对6种跨工况迁移诊断任务开展了验证实验。实验结果表明,所提方法的故障平均迁移识别率高达95.08%。该故障诊断方法实验研究涉及信号处理、深度学习等领域知识,有助于学生掌握基本原理,锻炼理论联系实际的能力。[Objective]Rolling bearings of motors are susceptible to failures owing to severe working environments and load fluctuations.A delay in dealing with this will lead to economic loss or even endanger personal safety.In recent years,deep learning has been broadly applied in rolling bearing fault diagnosis.However,traditional methods require training and test data to observe the same distribution,constraining their diagnostic ability under diverse operating conditions.To solve this problem,this paper presents a transfer learning approach that fuses angular domain resampling and domain-adversarial neural networks to lessen the distribution inconsistencies of data between dissimilar operating conditions and to achieve the cross-working-condition fault transfer diagnosis of rolling bearing faults.Moreover,the study of the proposed approach and the development of the associated experimental program aim to deepen students’understanding of signal processing and artificial intelligence theory and promote their learning enthusiasm.[Methods]First,the time–domain vibration signals and rotational speed pulse signals of the motor under diverse rotational speed conditions are synchronously gathered,and the rotational speed information is used to determine the rotational speed change curve of the motor.Next,the time–domain vibration signals are resampled in the angular domain according to this curve to acquire the angular domain vibration signals under diverse rotational speed conditions.This step aims to lessen the effect of rotational speed adjustment on the time–frequency properties of vibration signals and lessen the time–frequency variance of vibration signals under diverse rotational speed conditions.Then,the angular domain vibration signals under diverse rotational speed conditions are set as the source and target domains,respectively.The domain-invariant features in the source and target domain data are obtained using the domain-adversarial learning strategy,which reduces the data distribution variances between d

关 键 词：故障诊断 迁移学习 滚动轴承 实验设计与分析 

分 类 号：TM343[电气工程—电机]