期刊
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
卷 14, 期 7, 页码 3235-3243出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2018.2809730
关键词
Deep learning; output prediction; soft sensor; stacked autoencoder (SAE); variable-wise weighted SAE (VW-SAE)
类别
资金
- National Natural Science Foundation of China (NSFC) [61703440]
- Major Project of the NSFC [61590921]
- 111 project [B17048]
- Foundation for Innovative Research Groups of the NSFC [61621062]
- Innovation-driven plan in Central South University [2018CX011]
In modern industrial processes, soft sensors have played an important role for effective process control, optimization, and monitoring. Feature representation is one of the core factors to construct accurate soft sensors. Recently, deep learning techniques have been developed for high-level abstract feature extraction in pattern recognition areas, which also have great potential for soft sensing applications. Hence, deep stacked autoencoder (SAE) is introduced for soft sensor in this paper. As for output prediction purpose, traditional deep learning algorithms cannot extract high-level output-related features. Thus, a novel variable-wise weighted stacked autoencoder (VW-SAE) is proposed for hierarchical output-related feature representation layer by layer. By correlation analysis with the output variable, important variables are identified from other ones in the input layer of each autoencoder. The variables are assigned with different weights accordingly. Then, variable-wise weighted autoencoders are designed and stacked to form deep networks. An industrial application shows that the proposed VW-SAE can give better prediction performance than the traditional multilayer neural networks and SAE.
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