Chinese Journal of Chemical Engineering ›› 2020, Vol. 28 ›› Issue (12): 3061-3069.doi: 10.1016/j.cjche.2020.07.047

• Process Systems Engineering and Process Safety • Previous Articles     Next Articles

A subspace ensemble regression model based slow feature for soft sensing application

Qiong Jia, Jun Cai, Xinyi Jiang, Shaojun Li   

  1. Key Laboratory of Advanced Control and Optimization for Chemical Processes, East China University of Science and Technology, Ministry of Education, Shanghai 200237, China
  • Received:2019-12-29 Revised:2020-05-30 Online:2020-12-28 Published:2021-01-11
  • Contact: Shaojun Li
  • Supported by:
    The authors of this paper appreciate the support from the National Natural Science Foundation of China (No. 21676086).

Abstract: A novel adaptive subspace ensemble slow feature regression model was developed for soft sensing application. Compared to traditional single models and random subspace models, the proposed method is improved in three aspects. Firstly, sub-datasets are constructed through slow feature directions and variables in each subdatasets are selected according to the output related importance index. Then, an adaptive slow feature regression is presented for sub-models. Finally, a Bayesian inference strategy based on a slow feature analysis process that monitors statistics is developed for probabilistic combination. Two industrial examples were used to evaluate the proposed method.

Key words: Soft sensing, Slow feature regression, Subspace modeling, Ensemble learning