Dynamic Vision Enabled Contactless Cross-Domain Machine Fault Diagnosis with Neuromorphic Computing

被引:34
作者
Chen, Xinrui [1 ]
Li, Xiang [1 ]
Yu, Shupeng [1 ]
Lei, Yaguo [1 ]
Li, Naipeng [1 ]
Yang, Bin [1 ]
机构
[1] Xi An Jiao Tong Univ, Key Lab, Educ Minist Modern Design & Rotor Bearing Syst, Xian 710049, Peoples R China
基金
国家重点研发计划;
关键词
D O I
10.1109/JAS.2023.124107
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Dear Editor, This letter presents a novel dynamic vision enabled contactless cross-domain fault diagnosis method with neuromorphic computing. The event-based camera is adopted to capture the machine vibration states in the perspective of vision. A specially designed bio-inspired deep transfer spiking neural network (SNN) model is proposed for processing the event streams of visionary data, feature extraction and fault diagnosis. The proposed method can also extract domain-invariant features from different machine operating conditions without target-domain machine faulty data. Experiments on rotating machines are carried out for validations of the proposed method, and the proposed method is verified to be effective in contactless fault diagnosis.
引用
收藏
页码:788 / 790
页数:3
相关论文
共 10 条
[1]  
Helm D, 2023, Journal of Dynamics Monitoring and Diagnostics, DOI [10.37965/jdmd.2023.231, 10.37965/jdmd.2023.231]
[2]   Comparing Reservoir Artificial and Spiking Neural Networks in Machine Fault Detection Tasks [J].
Kholkin, Vladislav ;
Druzhina, Olga ;
Vatnik, Valerii ;
Kulagin, Maksim ;
Karimov, Timur ;
Butusov, Denis .
BIG DATA AND COGNITIVE COMPUTING, 2023, 7 (02)
[3]  
Lee M. Kim, 2022, Reliability Engineering & SystemSafety, V218
[4]   Intelligent Machinery Fault Diagnosis With Event-Based Camera [J].
Li, Xiang ;
Yu, Shupeng ;
Lei, Yaguo ;
Li, Naipeng ;
Yang, Bin .
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2024, 20 (01) :380-389
[5]   A robust intelligent fault diagnosis method for rolling element bearings based on deep distance metric learning [J].
Li, Xiang ;
Zhang, Wei ;
Din, Qian .
NEUROCOMPUTING, 2018, 310 :77-95
[6]  
Peng DD, 2023, Journal of Dynamics Monitoring and Diagnostics, DOI [10.37965/jdmd.2023.156, DOI 10.37965/JDMD.2023.156]
[7]   Improved spiking neural network for intershaft bearing fault diagnosis [J].
Wang, Jun ;
Li, Tianfu ;
Sun, Chuang ;
Yan, Ruqiang ;
Chen, Xuefeng .
JOURNAL OF MANUFACTURING SYSTEMS, 2022, 65 :208-219
[8]   Deep Spiking Residual Shrinkage Network for Bearing Fault Diagnosis [J].
Xu, Zongtang ;
Ma, Yumei ;
Pan, Zhenkuan ;
Zheng, Xiaoyang .
IEEE TRANSACTIONS ON CYBERNETICS, 2024, 54 (03) :1608-1613
[9]   Data privacy preserving federated transfer learning in machinery fault diagnostics using prior distributions [J].
Zhang, Wei ;
Li, Xiang .
STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL, 2022, 21 (04) :1329-1344
[10]   Data-driven based fault prognosis for industrial systems: a concise overview [J].
Zhong, Kai ;
Han, Min ;
Han, Bing .
IEEE-CAA JOURNAL OF AUTOMATICA SINICA, 2020, 7 (02) :330-345