基于多特征空间自适应网络的谐波减速器故障诊断  

Fault diagnosis of harmonic reducer based on multiple feature spaces adaptive network

作　　者：陈仁祥 张晓 李嘉琳 杨宝军 张旭[1] CHEN Renxiang;ZHANG Xiao;LI Jialin;YANG Baojun;ZHANG Xu(Chongqing Engineering Laboratory for Transportation Engineering Application Robot,Chongqing Jiaotong University,Chongqing 400074,China;Chongqing Robotics Institute,Chongqing 400714,China)

机构地区：[1]重庆交通大学交通工程应用机器人重庆市工程实验室,重庆400074 [2]重庆智能机器人研究院,重庆400714

出　　处：《振动工程学报》2025年第2期432-440,共9页Journal of Vibration Engineering

基　　金：国家自然科学基金资助项目(52475548);国家重点研发计划资助项目(2023YFB3406200);重庆市教委科学技术研究项目(KJZD-M202200701);重庆市自然科学基金创新发展联合基金资助项目(CSTB2023NSCQLZX0127);重庆市研究生联合培养基地项目(JDLHPYJD2024006)。

摘　　要：由于多测点位置不同引起的数据分布差异造成谐波减速器故障诊断效果不佳,提出基于多特征空间自适应网络(multiple feature spaces adaptation network,MFSAN)的谐波减速器故障诊断方法。对谐波减速器振动信号进行连续小波变换,以构造时频图来描述其运行状态特征。将不同位置传感器所测数据划分为多个源域数据和目标域数据映射到不同特征空间,得到不同测点位置下的特征表示。利用自适应网络将源域中学习到的知识自动应用到目标域,以自动对齐特定领域的特征分布,从而学习多个域不变表示。利用领域特定的决策边界来对齐分类器的输出,从而有效减少因传感器位置差异引起的数据分布差异。在工业机器人谐波减速器诊断实验中,所提诊断方法达到了99.72%的准确率,高于其他对比方法,验证了所提诊断方法的有效性和可行性。Due to the differences in data distribution caused by different locations of multiple measuring points,the fault diagnosis of the harmonic reducer is often ineffective.A fault diagnosis method for the harmonic reducer,based on a multiple feature spaces adaptation network(MFSAN),is proposed.Firstly,the vibration signal of the harmonic reducer is transformed using continuous wavelet transform to construct a time-frequency diagram that characterizes its operational state.Secondly,the data measured by sensors at different positions are divided into multiple source domain and target domain data,which are mapped to different feature spaces to obtain feature representations for each measuring point position.Then,the adaptive network is used to automatically transfer the knowledge learned from the source domain to the target domain features and automatically align the feature distribution of a specific domain to learn multiple domain-invariant representations.Finally,a domain-specific decision boundary is used to align the output of the classifier,effectively solving the data distribution differences caused by sensor location.Experimental results of harmonic reducer diagnosis of an industrial robot show that the identification accuracy of this method is 99.72%,which is higher than that of other comparison methods.The effectiveness and feasibility of this method are thus verified.

关 键 词：故障诊断 谐波减速器 连续小波变换 多特征空间自适应 

分 类 号：TH165.3[机械工程—机械制造及自动化] TH132.46