Automatic Deep Extraction of Robust Dynamic Features for Industrial Big Data Modeling and Soft Sensor Application

Abstract

Dynamic is one of the main bottlenecks in the industrial soft sensor application, due to the difficulties in representing and extracting dynamic data features. Meanwhile, an end-to-end deep network owns the ability to characterize sequence data information, but its fitting ability requires improvements in practical applications. In this article, an ensemble tree model with transferable and robust dynamic features extracted by a newly developed automatic dynamic feature extractor is proposed. First, the dynamic feature extractor with an encoding–decoding structure can provide effective dynamic features, which is equivalent to crossing and nonlinear mapping of sequences under the supervision of a decoder. Meanwhile, a new “regularization” method by smoothing dynamic features based on attention weights is proposed to denoise and alleviate the overfitting of the regressor after adding new features. Then, the extracted dynamic features can be transferred to the regressor with strong generalization ability, which takes into account the feature extraction of the deep network and the generalization of strong models. Finally, application results on a debutanizer distillation process show that the incorporation of robust dynamic features can significantly improve the soft sensing performance, compared to traditional methods. Moreover, the proposed model is further implemented through a cloud computing platform for industrial big data analytics.