DYNAMIC VISION ENABLED CONTACTLESS CROSS-DOMAIN MACHINE FAULT DIAGNOSIS WITH NEUROMORPHIC COMPUTING
XI’AN JIAOTONG UNIVERSITY
Xinrui Chen, Xiang Li, Shupeng Yu, Yaguo Lei, Naipeng Li, Bin Yang
ABSTRACT
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.
INVENTORS COMMUNITY
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