.png){kind=icon}
.png){kind=icon}
Return
A Data-Driven Soft Sensor Modeling Method Based on Deep Learning and its Application
.png){kind=icon}
PRE
AI
DOI：10.1109/TIE.2016.2622668.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
Deep learning
denoising autoencoder (DAE)
soft sensor
unlabeled data
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.2
Papers:
1.8W
Citations:
9.8W