Dynamic Vision-Based Machinery Fault Diagnosis With Cross-Modality Feature Alignment  

作　　者：Xiang Li Shupeng Yu Yaguo Lei Naipeng Li Bin Yang 

机构地区：[1]Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System,Xi’an Jiaotong University,Xi’an 710049,China [2]IEEE

出　　处：《IEEE/CAA Journal of Automatica Sinica》2024年第10期2068-2081,共14页自动化学报（英文版）

基　　金：supported by the National Science Fund for Distinguished Young Scholars of China(52025056);the China Postdoctoral Science Foundation(2023M732789);the China Postdoctoral Innovative Talents Support Program(BX20230290);the Fundamental Research Funds for the Central Universities(xzy012022062).

摘　　要：Intelligent machinery fault diagnosis methods have been popularly and successfully developed in the past decades,and the vibration acceleration data collected by contact accelerometers have been widely investigated.In many industrial scenarios,contactless sensors are more preferred.The event camera is an emerging bio-inspired technology for vision sensing,which asynchronously records per-pixel brightness change polarity with high temporal resolution and low latency.It offers a promising tool for contactless machine vibration sensing and fault diagnosis.However,the dynamic vision-based methods suffer from variations of practical factors such as camera position,machine operating condition,etc.Furthermore,as a new sensing technology,the labeled dynamic vision data are limited,which generally cannot cover a wide range of machine fault modes.Aiming at these challenges,a novel dynamic vision-based machinery fault diagnosis method is proposed in this paper.It is motivated to explore the abundant vibration acceleration data for enhancing the dynamic vision-based model performance.A crossmodality feature alignment method is thus proposed with deep adversarial neural networks to achieve fault diagnosis knowledge transfer.An event erasing method is further proposed for improving model robustness against variations.The proposed method can effectively identify unseen fault mode with dynamic vision data.Experiments on two rotating machine monitoring datasets are carried out for validations,and the results suggest the proposed method is promising for generalized contactless machinery fault diagnosis.

关 键 词：Condition monitoring domain generalization eventbased camera fault diagnosis machine vision 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TH17[自动化与计算机技术—计算机科学与技术]