Gated Quality-Driven Feature Extraction With A hybrid SIAE Model for Industrial Soft Sensor

Nowadays, deep learning has been widely applied to estimate the hard-to-measure quality variables in industrial processes due to its excellent non-linear modeling and feature extraction capabilities. Stacked isomorphic autoencoder (SIAE) is one of deep learning methods, which minimizes the reconstruction error of the original input variables during the pre-training phase, extracting features from low to high layers. Despite this, the features extracted in this manner, in fact, disregard the information associated with the quality variables, which have an impact on the effectiveness of the succeeding supervised fine-tuning phase. Besides, only the features extracted by the last IAE are used to predict the quality variables, which leads to poor prediction results. Therefore, a hybrid stacked isomorphic autoencoder (HSIAE) is developed in this paper to implement gated quality-driven feature extraction. During the hierarchical pre-training phase, HSIAE can extract more features associated with quality variables by incorporating the error of predicting quality variables into the loss function of SIAE. Subsequently, gated units leverage the different hidden features to the fullest and quantifying their contributions. Finally, the feasibility and effectiveness of the proposed HSIAE model are validated with debutanizer column dataset.