Fault detection and recognition of multivariate process based on feature learning of one-dimensional convolutional neural network and stacked denoised autoencoder

Abstract

Multivariate process pattern recognition (MPPR) is essential towards continuous quality control task. A challenging problem is to extract effective features from complex process signals with high-dimensional and nonlinear characteristics. This affects effectiveness of various classifiers in process fault detection and diagnosis significantly. In this paper, we propose a hybrid deep learning model (i.e. 1-DCNN + SDAE) that integrates one-dimensional convolutional neural network (1-DCNN) and stacked denoising auto-encoders (SDAE) to extract high level features from complex process signals. In comparison with two-dimensional images, one-dimensional process signals allow not only to extract spatial features, but also reduce calculation cost. 1-DCNN is capable of extracting representative features from one-dimensional process signals and then improves MPPR performance of classifiers significantly. SDAE is embedded after fully connected layer of 1-DCNN for further dimension reduction and feature extraction. 1-DCNN + SDAE preserves advantages of 1-DCNN and SDAE for feature learning from high-dimensional data. This makes it be flexible for those process fault detection and diagnosis tasks. The effectiveness of 1-DCNN + SDAE is validated on a complex numerical process, two process benchmarks i.e. Tennessee Eastman process (TEP) and Fed-batch fermentation penicillin process (FBFP), and a real-life manufacturing case of industrial conveyor belt. The experimental results illustrate effectiveness of the proposed method for feature learning and fault diagnosis on multivariate manufacturing processes. The comparison between 1-DCNN + SDAE and other typical DNNs on these processes, indicates the effectiveness of the proposed method for process fault detection and diagnosis. This study will provide the guidance for development of hybrid deep learning-based multivariate control models.