Long–short-term memory encoder–decoder with regularized hidden dynamics for fault detection in industrial processes. (April 2023)

• Record Type: Journal Article
• Authors: Liu, Yingxiang; Young, Robert; Jafarpour, Behnam
• Is Part Of: Journal of process control. Volume 124(2023)
• Page Start: 166
• Page End: 178
• Publication Date: 2023-04
• Subjects: Recurrent neural network -- Fault detection -- Process monitoring -- Tennessee Eastman process; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2023.01.015
• Languages: English
• ISSNs: 0959-1524

Abstract

Abstract: The ability of recurrent neural networks (RNN) to model nonlinear dynamics of high dimensional process data has enabled data-driven RNN-based fault detection algorithms. Previous studies have focused on detecting faults by identifying the discrepancies in data distribution between the faulty and normal data, as reflected in prediction errors generated by RNN models. However, in industrial processes, variations in data distribution can also result from changes in normal control setpoints and compensatory control adjustments in response to disturbances, making it hard to differentiate between normal and faulty conditions. This paper proposes a fault detection method utilizing a long short-term memory (LSTM) encoder–decoder structure with regularized hidden dynamics and reversible instance normalization (RevIN) to compactly represent high-dimensional measurements for effective monitoring. During training, the hidden states of the model are regularized to form a low-dimensional latent space representation of the original multivariate time series data. As a result, the prediction errors of the latent states can be used to monitor the abnormal dynamic variations, while the reconstruction errors of the measured variables are used to monitor the abnormal static variations. Furthermore, the proposed indices can reflect operating conditions, even when the distribution of test data changes, which helps distinguish faults from normal adjustments and disturbances that controllers can settle. Data from numerical simulation and the Tennessee Eastman process are used to illustrate the effectiveness of the proposed fault detection method. Highlights: Regularized LSTM encoder–decoder represents latent space dynamcis of time series data. Two monitoring statistics detect abnormal dynamics and static relationships. Faults are distinguished from disturbances by accounting for the effect of feedback controller.