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.

中文翻译：

---

动态视觉通过神经形态计算实现非接触式跨域机器故障诊断

尊敬的编辑，这封信提出了一种新颖的动态视觉、基于神经形态计算的非接触式跨域故障诊断方法。采用基于事件的相机从视觉角度捕捉机器振动状态。提出了一种专门设计的仿生深度传输尖峰神经网络（SNN）模型，用于处理视觉数据的事件流、特征提取和故障诊断。该方法还可以从不同的机器运行条件中提取域不变特征，而无需目标域机器故障数据。通过旋转电机实验验证了该方法的有效性，验证了该方法在非接触式故障诊断中的有效性。