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.
