Fault Diagnostic Method Based on Deep Learning and Multimodel Feature Fusion for Complex Industrial Processes

Fault diagnostic methods based on deep learning for industrial processes are becoming a research hotspot. Most existing methods focus on algorithmic improvements and attempt to establish a single model to extract effective features of faults. However, effective information related to different faults is diverse. Therefore, instead of using a single model to extract features and build a model to correctly diagnose all types of faults, we propose a novel fault diagnostic method based on deep learning and multimodel feature fusion. First, the minimum redundancy–maximum relevance method is used to select the variables that are the most relevant to each fault. Next, the features of each fault are extracted using a stack autoencoder, and the corresponding residual matrices are obtained. The features and residuals obtained using each model are then spliced as new inputs to establish a classifier for fault diagnosis. Finally, we apply the proposed method to the Tennessee Eastman benchmark process to demonstrate its performance and efficiency.