Explainable Health State Prediction for Social IoTs through Multi-Channel Attention

The core technology of Industry 4.0 is to enable the intelligence of manufacturing. One of the important tasks is anomaly detection. Although existing anomaly detection methods have achieved high accuracy, the basis of judgments cannot provide explainability, which greatly reduces the possibility for improving the model or facilitating human-machine cooperation. Therefore, in this paper, the goal is to provide the explainability for machine fault detection for social IoTs and realize the health monitoring and prognosis of the bearings simultaneously. Specifically, vibration signals from multiple sensors are transformed into spectrograms by short-time Fourier transform. Afterward, the features of frequency-domain data are extracted by the Squeeze-and-Excitation block and self-attention mechanism to assess the degradation of whole system. As such, when the process enters the early degradation, the source of components that causes the abnormality can be identified through the attention weight distribution. Experimental results show that the proposed approach achieves high accuracy in run-to-failure tests. Moreover, the proposed approach shows a better ability to explain the predicted results than the state-of-the-art bearing detection methods.