神经形态工程学
计算机科学
人工智能
领域(数学分析)
断层(地质)
机器视觉
特征提取
人工神经网络
事件(粒子物理)
特征(语言学)
计算机视觉
地震学
数学分析
数学
地质学
语言学
哲学
物理
量子力学
作者
X Chen,Xiang Li,Shupeng Yu,Yaguo Lei,Naipeng Li,Bin Yang
出处
期刊:IEEE/CAA Journal of Automatica Sinica
[Institute of Electrical and Electronics Engineers]
日期:2024-02-12
卷期号:11 (3): 788-790
被引量:20
标识
DOI:10.1109/jas.2023.124107
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.
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