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

作者：X Chen, Xiang Li, Shupeng Yu, Yaguo Lei, Naipeng Li, Bin Yang · 发表于：IEEE/CAA Journal of Automatica Sinica · 年份：2024 · DOI：10.1109/jas.2023.124107 · 被引用次数：57 · 研究领域：Advanced Memory and Neural Computing、Neural dynamics and brain function、Neuroscience and Neural Engineering

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.