The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
A Data-Driven Soft Sensor Modeling Method Based on Deep Learning and its Application
Abstract
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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A Review on Soft Sensors for Monitoring, Control, and Optimization of Industrial Processes
Jiang, Y., Yin, S., Dong, J., & Kaynak, O. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2020.3033153 · Cited by 667
A Survey on Deep Learning for Data-Driven Soft Sensors
Sun, Q., & Ge, Z. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3053128 · Cited by 665
Sensors and Techniques for On-Line Determination of Cell Viability in Bioprocess Monitoring
Rösner, L., Walter, F., Ude, C., John, G., & Beutel, S. (2022) · Bioengineering · doi:10.3390/bioengineering9120762 · Cited by 42
Enhancing vehicular emissions monitoring: A GA-GRU-based soft sensors approach for HDDVs
Yang, L., Ge, Y., Lyu, L., Tan, J., Hao, L., Wang, X., Yin, H., & Wang, J. (2024) · Environmental Research · doi:10.1016/j.envres.2024.118190 · Cited by 16
Metadata
- Authors
- Yan, W., Tang, D., & Lin, Y.
- Venue
- IEEE Transactions on Industrial Electronics
- Published
- 2017-05
- Volume
- 64
- Issue
- 5
- Pages
- 4237-4245
- ISSN
- 0278-0046 (ISSN-L)
- 1557-9948
- OpenAlex
- W2539756354
- Cited by
- 294
- References
- 29
- FWCI
- 14.87
- Type
- journal article
- Language
- en
- Open access
- closed
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