One-dimensional residual convolutional auto-encoder for fault detection in complex industrial processes

Abstract

Fault detection and diagnosis have always been the key techniques for safe and reliable operation of industrial processes. However, the high dimension and noise of process variables have brought great challenges to the fault detection model. In recent years, due to the powerful feature extraction ability, deep learning has been widely applied in process fault detection and diagnosis. However, these deep neural networks (DNNs) often need a large amount of label data for supervised training, or show poor performance in learning features under unsupervised-learning condition. This paper proposes a new DNN model, a one-dimension residual convolutional auto-encoder (1DRCAE), where unsupervised learning is used to extract representative features from complex industrial processes. 1DRCAE effectively integrates the one-dimensional convolutional kernel with an auto-encoder and is embedded residual learning block for effective feature extraction from one-dimensional data. The two statistics and squared prediction error are generated in the feature space and residual space of 1DRCAE, respectively. Finally, the feasibility and superiority of 1DRCAE are verified on a simulation process, Tennessee Eastman process, Fed-batch fermentation penicillin process, and a real-life case. The convolutional auto-encoder technique provides a new way for feature learning and fault detection on complex industrial processes.