A Data-Driven Soft Sensor Modeling Method Based on Deep Learning and its Application

Soft sensors have been widely used in industrial processes. The core issue of data-driven soft sensors is building soft sensor models with excellent performance and robustness. This paper introduces deep learning to soft sensor modeling and proposes a novel soft sensor modeling method based on a dee...

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Veröffentlicht in:IEEE transactions on industrial electronics (1982) Jg. 64; H. 5; S. 4237 - 4245
Hauptverfasser: Yan, Weiwu, Tang, Di, Lin, Yujun
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.05.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Biological system modeling
Computational modeling
Data models
Deep learning
denoising autoencoder (DAE)
Machine learning
Microprocessors
Modelling
Neural networks
Noise reduction
Oxygen content
Performance enhancement
Robustness
Sensors
soft sensor
Training
unlabeled data
ISSN:0278-0046, 1557-9948
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Zusammenfassung:Soft sensors have been widely used in industrial processes. The core issue of data-driven soft sensors is building soft sensor models with excellent performance and robustness. This paper introduces deep learning to soft sensor modeling and proposes a novel soft sensor modeling method based on a deep learning network that integrates denoising autoencoders with a neural network (DAE-NN). An improved gradient descent is employed to update the model parameters. The proposed modeling method is able to capture the essential information of input data through deep architecture, building soft sensors with excellent performance. The DAE-NN-based soft sensor is applied in practical applications to estimate the oxygen content in flue gasses in 1000-MW ultrasuperficial units. Comparing conventional soft sensor modeling methods, i.e., shallow learning methods, DAE-NN-based soft sensor significantly improves the performance and generalization of data-driven soft sensors. Deep learning provides a very effective and promising method for soft sensor modeling.
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ISSN:0278-0046
1557-9948
DOI:10.1109/TIE.2016.2622668