Automated feature learning for nonlinear process monitoring – An approach using stacked denoising autoencoder and k-nearest neighbor rule. (April 2018)

• Record Type: Journal Article
• Authors: Zhang, Zehan; Jiang, Teng; Li, Shuanghong; Yang, Yupu
• Is Part Of: Journal of process control. Volume 64(2018)
• Page Start: 49
• Page End: 61
• Publication Date: 2018-04
• Subjects: Deep learning -- Automated feature learning -- Stacked denoising autoencoder -- k-Nearest neighbor rule -- Nonlinear process monitoring; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2018.02.004
• Languages: English
• ISSNs: 0959-1524

Abstract

Highlights: Automated feature learning based on stacked denoising autoencoder (SDAE) and k-nearest neighbor rule (kNN) for nonlinear process monitoring. SDAE is used to automatically learn the patterns inherent in the nonlinear process and extract key features. New monitoring statistics that are HD 2 and RD 2 are constructed based on kNN rule using the extracted key features. The effectiveness of the proposed method is verified by two case studies including a nonlinear numerical example and TE benchmark process. Abstract: Modern industrial processes have become increasingly complicated, consequently, the nonlinearity of data collected from these systems continues to increase. However, the feature extraction methods of existing process monitoring are not capable of extracting crucial features from these highly nonlinear data, which affects the performance of monitoring. In this paper, a novel nonlinear process monitoring method based on stacked denoising autoencoder (SDAE) and k-nearest neighbor (kNN) rule is proposed. Specifically, stacked denoising autoencoder is utilized to model the nonlinear process data and automatically extract crucial features. The original nonlinear space is then mapped to the feature space and the residual space via SDAE. Two new statistics in the above spaces are constructed by introducing the kNN rule with their corresponding control limits determined by kernel density estimation. Case studies on a nonlinear numerical system and the Tennessee Eastman benchmark process verify the effectiveness of the proposed method.