Manifold regularized stacked autoencoders-based feature learning for fault detection in industrial processes

Abstract

Multivariate statistical process control (MSPC) has been widely employed for process fault detection. Recently, deep neural networks (DNNs), i.e., stacked autoencoder (SAE) enjoys its popularization in process fault detection. SAE shows good performance in extracting representative features from the process data based on unsupervised learning, which provides a new monitoring method without large amount of labeled data. However, the extraction of the intrinsic geometrical information from process signals is not considered by these regular SAEs This paper proposes a new DNN, manifold regularized stacked autoencoders (MRSAE) for fault detection in complex industrial processes. The local/global information preservation is incorporated into the encoding phase of SAE to capture intrinsic structure of the process data. MRSAE is used to describe distribution of the nonlinear process data and learn effective features for process fault detection. Two typical statistics (i.e., Hotelling’s T-squared (T2) and squared prediction error (SPE)) based on the extracted features by MRSAE are developed for process fault detection. The comparison between MRSAE and other typical DNNs on a complex numerical process and two benchmark processes, i.e., Tennessee Eastman process (TEP) and Fed-Batch fermentation penicillin process (FBFP), indicates the effectiveness of the proposed method for process fault detection. The manifold regularization-based DNN technique provides a new way for feature learning from high-dimensional and nonlinear process signals.