Gated Stacked Target-Related Autoencoder: A Novel Deep Feature Extraction and Layerwise Ensemble Method for Industrial Soft Sensor Application

• Published in: IEEE transactions on cybernetics Vol. 52; no. 5; pp. 3457 - 3468
• Main Authors: Sun, Qingqiang, Ge, Zhiqiang
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.05.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Computational modeling; Data mining; Data models; Deep learning; Deep learning (DL); Feature extraction; gated neurons; Information flow; Logic gates; Machine learning; Modelling; Neurons; nonlinear feature extraction; Process control; Representations; soft sensor; stacked autoencoder (SAE); target-related information
• ISSN: 2168-2267; 2168-2275; 2168-2275
• DOI: 10.1109/TCYB.2020.3010331

These days, data-driven soft sensors have been widely applied to estimate the difficult-to-measure quality variables in the industrial process. How to extract effective feature representations from complex process data is still the difficult and hot spot in the soft sensing application field. Deep learning (DL), which has made great progresses in many fields recently, has been used for process monitoring and quality prediction purposes for its outstanding nonlinear modeling and feature extraction abilities. In this work, deep stacked autoencoder (SAE) is introduced to construct a soft sensor model. Nevertheless, conventional SAE-based methods do not take information related to target values in the pretraining stage and just use the feature representations in the last hidden layer for final prediction. To this end, a novel gated stacked target-related autoencoder (GSTAE) is proposed for improving modeling performance in view of the above two issues. By adding prediction errors of target values into the loss function when executing a layerwise pretraining procedure, the target-related information is used to guide the feature learning process. Besides, gated neurons are utilized to control the information flow from different layers to the final output neuron that take full advantage of different levels of abstraction representations and quantify their contributions. Finally, the effectiveness and feasibility of the proposed approach are verified in two real industrial cases.