Multi‐block gated recurrent unit network‐based fault diagnosis method for multivariate nonlinear process

Abstract

AbstractThe increasing complexity of industrial processes brings new challenges to fault diagnosis tasks, and different types of faults have higher and higher requirements on the performance of fault classification models. This paper proposes a novel multivariate nonlinear temporal‐related fault diagnosis method based on gated recurrent units (GRUs). First, to improve the performance of the model in local information extraction and global integration, high dimensional variables are divided into multiple sub‐blocks according to the structure of chemical process units, and a new block normalization method is proposed to improve the performance of local feature extraction. Second, aiming at the slow drifting faults, the GRU network is adopted inside the sub‐block to extract local sparse and nonlinear temporal features. By combining the variance features of variables after block normalization, the performance of the model on multiplicative faults will improve. Finally, aiming at the complex correlation between variables, a new recurrent matrix method is proposed to extract the time transform information inside each variable to improve the comprehensive performance of the model. Through a multi‐level feature integration strategy, the model can be trained in parallel to improve the training speed. The proposed method shows good performance in the Tennessee Eastman process, and the extracted multi‐class features allow the model to be trained end‐to‐end and simultaneously diagnose multiple types of faults.