Fault diagnostic method based on transfer dynamic deep learning for few shot temporal–spatial correlation industry process

Abstract

Data-based fault diagnosis plays a crucial role in ensuring the safety of industrial processes. However, the complex industry process often has temporal–spatial correlation with insufficient labelled fault data. To settle these problems, a new transfer dynamic deep learning strategy that combines autoencoder (AE) with gate recurrent unit (GRU) is proposed. First, dynamic AE networks are introduced to extract the single-attribute time series features, and the dynamic GRU is employed to extract the spatial correlation features among multiple feature dimensions and temporal correlation among samples. Then, to solve the problem of insufficiently labelled industrial data, the model-based transfer learning between the sufficient laboratory data and insufficient labelled industrial data is executed. Experimental results based on the Tennessee Eastman (TE) process and the benchmark simulation model 1 (BSM1) process show that the proposed approach has excellent performance.