Dynamic Feature Extraction and Semi-Supervised Soft Sensor Model Based on SCINet for Industrial and Transportation Processes

In industrial processes, dynamic changes are one of the factors restricting the performance of soft sensor models. Meanwhile, the inconsistency of sensor sampling rates often leads to the problem of mismatch between process variables and quality variables. This paper proposes a semi-supervised soft sensor modeling method based on sample convolution and interactive networks (SCINet). To extract the dynamic information of industrial processes more fully, an unsupervised time series dynamic feature extractor was designed based on SCINet and an autoencoder, and the feature extractor was trained using complete data. The dynamic features encoded by the dynamic feature extractor were transferred to the eXtreme Gradient Boosting (XGBoost) ensemble model with strong generalization ability. The semi-supervised soft measurement model SSCI-XGBoost was established. The effectiveness of dynamic feature transfer and model performance improvement was verified on the industrial process dataset.