A Multi-Sensor Feature Representation and Fusion Method for Data-Driven Industrial Process Fault Diagnosis

Data-driven fault diagnosis is a critical component of industrial process monitoring. To more effectively capture the temporal relationships within multi-sensor data and the spatial relationships among sensors, this paper proposed a feature representation and fusion method for fault diagnosis in complex industrial processes. Firstly, a Gramian Angular Field (GAF) method with temporal weighting (TGAF) is developed to convert process time series signals into image sequences, where each sub-image is assigned corresponding temporal weights. Subsequently, features are represented from each sensor's image sequence, and a spatial-weighted multi-channel convolutional neural network (SMCNN) is employed to fuse these features and generate new feature embeddings. Finally, a fault diagnosis model is jointly trained to optimize and balance the weight loss across multiple channels. The effectiveness of the proposed method is validated by utilizing the Tennessee-Eastman (TE) process and Vinyl Acetate Monomer (VAM) process, showing significant enhancements in fault diagnosis accuracy and reliability compared to other similar methods.