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Quality-related locally weighted soft sensing for non-stationary processes by a supervised Bayesian network with latent variables

Journal Article


Abstract


  • Soft sensors are widely used to predict quality variables which are usually hard to measure. It is necessary to construct an adaptive model to cope with process non-stationaries. In this study, a novel quality-related locally weighted soft sensing method is designed for non-stationary processes based on a Bayesian network with latent variables. Specifically, a supervised Bayesian network is proposed where quality-oriented latent variables are extracted and further applied to a double-layer similarity measurement algorithm. The proposed soft sensing method tries to find a general approach for non-stationary processes via quality-related information where the concepts of local similarities and window confidence are explained in detail. The performance of the developed method is demonstrated by application to a numerical example and a debutanizer column. It is shown that the proposed method outperforms competitive methods in terms of the accuracy of predicting key quality variables.

Publication Date


  • 2021

Citation


  • Xu, Y., Wang, Y., Yan, T., He, Y., Wang, J., Gu, D., . . . Li, W. (2021). Quality-related locally weighted soft sensing for non-stationary processes by a supervised Bayesian network with latent variables. Frontiers of Information Technology and Electronic Engineering, 22(9), 1234-1246. doi:10.1631/FITEE.2000426

Scopus Eid


  • 2-s2.0-85115199975

Start Page


  • 1234

End Page


  • 1246

Volume


  • 22

Issue


  • 9

Abstract


  • Soft sensors are widely used to predict quality variables which are usually hard to measure. It is necessary to construct an adaptive model to cope with process non-stationaries. In this study, a novel quality-related locally weighted soft sensing method is designed for non-stationary processes based on a Bayesian network with latent variables. Specifically, a supervised Bayesian network is proposed where quality-oriented latent variables are extracted and further applied to a double-layer similarity measurement algorithm. The proposed soft sensing method tries to find a general approach for non-stationary processes via quality-related information where the concepts of local similarities and window confidence are explained in detail. The performance of the developed method is demonstrated by application to a numerical example and a debutanizer column. It is shown that the proposed method outperforms competitive methods in terms of the accuracy of predicting key quality variables.

Publication Date


  • 2021

Citation


  • Xu, Y., Wang, Y., Yan, T., He, Y., Wang, J., Gu, D., . . . Li, W. (2021). Quality-related locally weighted soft sensing for non-stationary processes by a supervised Bayesian network with latent variables. Frontiers of Information Technology and Electronic Engineering, 22(9), 1234-1246. doi:10.1631/FITEE.2000426

Scopus Eid


  • 2-s2.0-85115199975

Start Page


  • 1234

End Page


  • 1246

Volume


  • 22

Issue


  • 9