Deep learning for fault-relevant feature extraction and fault classification with stacked supervised auto-encoder. (August 2020)

• Record Type: Journal Article
• Authors: Wang, Yalin; Yang, Haibing; Yuan, Xiaofeng; Shardt, Yuri A.W.; Yang, Chunhua; Gui, Weihua
• Is Part Of: Journal of process control. Volume 92(2020)
• Page Start: 79
• Page End: 89
• Publication Date: 2020-08
• Subjects: Process monitoring -- Fault classification -- Stacked auto-encoder (SAE) -- Tennessee–Eastman process -- Deep learning; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2020.05.015
• Languages: English
• ISSNs: 0959-1524

Abstract

Abstract: Stacked auto-encoder (SAE)-based deep learning has been introduced for fault classification in recent years, which has the potential to extract deep abstract features from the raw input data. However, SAE cannot ensure the relevance of deep features with the fault types due to its unsupervised self-reconstruction in the pretraining stage. To overcome this problem, a stacked supervised auto-encoder is proposed to pretrain the deep network and obtain deep fault-relevant features from raw input data. In each supervised auto-encoder, informative features are learned from the input data with the goal that they can largely distinguish different fault types. By stacking multiple supervised auto-encoders hierarchically, high-level fault-relevant features are gradually learned from raw input data, which can improve the classification accuracy of the classifiers. The proposed SSAE is tested on the Tennessee–Eastman (TE) benchmark process and a real industrial hydrocracking process. The results show the effectiveness and flexibility of SSAE. Highlights: A stacked supervised auto-encoder is proposed to pretrain deep network and obtain deep fault-relevant features. In each supervised auto-encoder, informative features are learned from the input data with the goal that they can largely distinguish different fault types. High-level fault-relevant features are gradually learned from raw input data by hierarchically stacking multiple supervised auto-encoders. High classification performance of the proposed method is validated on TE process and an industrial hydrocracking process.