Hybrid Dual-Channel Attention CNN and eXtreme Gradient Boosting for Industrial Process Model Development and Fault Diagnosis

Fault diagnosis is important for ensuring the safe and stable operation of industrial equipment. Due to the characteristics of high dimensionality, nonlinearity and timing in modern industrial processes, the problems brought about by traditional fault diagnosis methods are becoming increasingly evident. Deep-learning-based fault diagnosis demonstrates excellent performance. However, most fault diagnosis models are insufficient in feature extraction and cannot remove more redundant feature information. Therefore, a new model integrating dual-channel attention convolutional neural network (ACNN) and eXtreme gradient boosting (XGBoost) algorithm was developed. Among them, the ACNN can enhance features through channel attention and spatial attention, reduce redundant information, and improve diagnostic performance. To obtain the optimal model, the Adam optimizer with better reliability is used to optimize the model. Finally, the optimal feature information collected by the ACNN is passed to the XGBoost classifier for classification. Extensive experiments on two benchmark datasets, the tennessee eastman (TE) process and a real-world industrial coking furnace, demonstrate that the proposed method achieves superior diagnostic accuracy of 95.64% and 97.44%. The proposed framework shows great potential for deployment in practical industrial scenarios where fast and accurate fault detection is essential.