基于信息融合子域适应的不同工况下谐波减速器故障诊断方法  

A fault diagnosis method for harmonic reducers under different operating conditions based on information fusion subdomain adaptation

作　　者：康守强[1] 章炜东 王玉静[1] 刘连胜 孙宇林[1] Kang Shouqiang;Zhang Weidong;Wang Yujing;Liu Liansheng;Sun Yulin(Heilongjiang Province Key Laboratory of Pattern Recognition and Information Perception,Harbin University of Science and Technology,Harbin 150080,China;School of Electronics and Information Engineering,Harbin Institute of Technology,Harbin 150001,China;Zhengzhou Research Institute,Harbin Institute of Technology,Zhengzhou 450000,China)

机构地区：[1]哈尔滨理工大学模式识别与信息感知黑龙江省重点实验室,哈尔滨150080 [2]哈尔滨工业大学电子与信息工程学院,哈尔滨150001 [3]哈尔滨工业大学郑州研究院,郑州450000

出　　处：《仪器仪表学报》2024年第3期60-71,共12页Chinese Journal of Scientific Instrument

基　　金：国家自然科学基金(52375533);山东省自然科学基金(ZR2023ME057);哈尔滨市制造业科技创新人才(2023CXRCCG017)项目资助。

摘　　要：针对工业机器人谐波减速器不同工况数据分布差异大,部分工况数据标签缺失以及单一传感器获取信息不全面,导致诊断准确率不高的问题,提出一种信息融合子域适应的不同工况下谐波减速器故障诊断方法。该方法将源域和目标域一维振动数据利用小波变换构建时频图;使用基于小波变换的图像融合方法整合多个传感器的时频信息并构建融合图像;提出多表示特征提取结构的改进残差网络以充分挖掘融合样本多表示特征,同时,在无监督场景下将源域和目标域融合样本的多表示特征进行子域适应处理,减小两域的各个子域间的分布差异,从而将知识从标签丰富的源域迁移到标签缺失的目标域,最终实现不同工况下谐波减速器的故障诊断。通过搭建工业机器人谐波减速器故障实验台并进行实测,所提方法在所有迁移任务中平均准确率可达98.8%,能够有效实现无监督场景中不同工况下谐波减速器的故障诊断。In response to the significant variations in data distribution of industrial robot harmonic reducers under different operating conditions,the partial absence of data labels for certain conditions,and the incomplete information obtained from a single sensor,which together result in low diagnostic accuracy,a fault diagnosis method is proposed based on information fusion and subdomain adaptation for different operating conditions of harmonic reducers.Time-frequency graphs are constructed using wavelet transform on one-dimensional vibration data from source and target domains.Time-frequency information from multiple sensors is integrated using a wavelet transform-based image fusion method,and the fused image is created.To fully exploit the multi-representational features of the fused samples,an improved residual network with a multi-representation feature extraction structure is proposed.Simultaneously,in an unsupervised scenario,the multi-representation features of the fused samples from the source and target domains are subjected to subdomain adaptation,for reducing the distribution differences between subdomains of both domains.Transfer the knowledge from the label-rich source domain to the label-deficient target domain,and ultimately fault diagnosis of harmonic reducers can be achieved under different operating conditions.By establishing an experimental platform for the industrial robot harmonic reducers and conducting actual measurements,the proposed method can achieve an average accuracy of 98.8%for all transfer tasks,and effectively enable fault diagnosis of harmonic reducers under different operating conditions in an unsupervised scenario.

关 键 词：信息融合 不同工况 域适应 谐波减速器 故障诊断 

分 类 号：TN911.7[电子电信—通信与信息系统] TH165.3[电子电信—信息与通信工程]