Deep Multiscale Convolutional Model With Multihead Self-Attention for Industrial Process Fault Diagnosis

In industrial fault diagnosis, traditional methods grapple with challenges, such as nonstationarity, nonlinearity, high dimensionality, and strong coupling. To address these issues, we propose an end-to-end fusion model based on multiscale residual convolutional channel attention and transformer model (MRCC-Transformer). This approach initially leverages a multiscale residual convolutional neural network (CNN) to extract data features across various scales, thereby preventing model degradation and autonomously learning and integrating abundant fault information from multiple monitoring variables. Subsequently, a channel attention mechanism (CAM) is introduced to prioritize focus on pertinent convolutional channels to enhance the network’s effectiveness and discriminative capacity. Furthermore, the Transformer is employed to establish dependencies among distinct features to enhance fault diagnosis accuracy. Lastly, the input data is classified for fault diagnosis. The efficacy of the proposed method was validated through simulation experiments on the Tennessee-Eastman (TE) process and an industrial coking furnace. Comparative results demonstrate that the proposed method significantly improves the accuracy of fault diagnosis.