机电工程技术2024，Vol.53Issue(2)：24-28,5.DOI:10.3969/j.issn.1009-9492.2024.02.005
基于半监督学习Informer算法的工业机器人故障诊断方法
Informer-based Semi Supervised Learning for Fault Diagnosis of Industrial Robot
摘要
Abstract
In industrial fields,it is difficult to collect large amounts of labeled fault data for 6-axis industrial robots.Traditional intelligent diagnosis methods usually rely on supervised learning with large-scale labeled data,but this has limitations in practical applications.In order to address this problem while tackling the issues of insufficient feature extraction capabilities and poor classification performance of individual models,by combining semi-supervised learning mechanisms with Informer's advantages in processing time series data,a semi-supervised learning and probabilistic sparse attention-based Informer network architecture model is proposed to achieve deep learning on small amounts of labeled data and large amounts of unlabeled data to realize accurate fault diagnosis.The test data collected in multiple sets of real environments are verified,by comparing with CNN,LSTM and GRU networks on distinguishing different fault severity levels,the proposed method achieved 90%diagnosis accuracy under the 100%unlabeled data setting,with higher classification accuracy and faster convergence;with 10%labeled data,the proposed method attained 89.7%diagnosis accuracy.关键词
深度学习/故障诊断/半监督学习/无标签数据/工业机器人Key words
deep learning/fault diagnosis/semi-supervised learning/unlabeled data/industrial robot分类
信息技术与安全科学引用本文复制引用
宋俊杰,陈翀,王涛,程良伦..基于半监督学习Informer算法的工业机器人故障诊断方法[J].机电工程技术,2024,53(2):24-28,5.基金项目
广东省自然科学基金项目(2020JJ4316) （2020JJ4316）
