Journal
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
Volume 33, Issue 12, Pages 7598-7609Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2021.3085869
Keywords
Probabilistic logic; Data models; Feature extraction; Training; Sensors; Adaptation models; Transfer learning; Deep learning; industrial processes; missing data; probabilistic transfer learning (TL); soft sensor
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Funding
- NSFC-Zhejiang Joint Fund for the Integration of Industrialization and Informatization [U1709211]
- Research Project of the State Key Laboratory of Industrial Control Technology, Zhejiang University, China [ICT2021A15]
- Open Research Project of the State Key Laboratory of Industrial Control Technology, Zhejiang University, China [ICT2021B52]
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This article introduces deep transfer learning to soft sensor modeling and proposes a deep probabilistic transfer regression framework, which effectively transfers knowledge from a related source to enhance soft sensing performance.
Soft sensors have been extensively developed and applied in the process industry. One of the main challenges of the data-driven soft sensors is the lack of labeled data and the need to absorb the knowledge from a related source operating condition to enhance the soft sensing performance on the target application. This article introduces deep transfer learning to soft sensor modeling and proposes a deep probabilistic transfer regression (DPTR) framework. In DPTR, a deep generative regression model is first developed to learn Gaussian latent feature representations and model the regression relationship under the stochastic gradient variational Bayes framework. Then, a probabilistic latent space transfer strategy is designed to reduce the discrepancy between the source and target latent features such that the knowledge from the source data can be explored and transferred to enhance the target soft sensor performance. Besides, considering the missing values in the process data in the target operating condition, the DPTR is further extended to handle the missing data problem utilizing the strong generation and reconstruction capability of the deep generative model. The effectiveness of the proposed method is validated through an industrial multiphase flow process.
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