A novel data-based quality-related fault diagnosis scheme for fault detection and root cause diagnosis with application to hot strip mill process. (October 2017)

• Record Type: Journal Article
• Authors: Ma, Liang; Dong, Jie; Peng, Kaixiang; Zhang, Kai
• Is Part Of: Control engineering practice. Volume 67(2017)
• Page Start: 43
• Page End: 51
• Publication Date: 2017-10
• Subjects: Quality-related -- Root cause diagnosis -- Hot strip mill process -- Modified canonical variable analysis -- Generalized reconstruction based contribution; Automatic control -- Periodicals; 629.89
• DOI: 10.1016/j.conengprac.2017.07.005
• Languages: English
• ISSNs: 0967-0661

Abstract

Abstract: In this paper, a new technology or solution of quality-related fault diagnosis is provided for hot strip mill process (HSMP). Different from traditional data-based fault diagnosis methods, the alternative approach is focused more on root cause diagnosis. The new scheme addresses the quality-related fault detection with the developed modified canonical variable analysis (MCVA) model, then the advantage of original generalized reconstruction based contribution (GRBC) is followed to identify the faulty variables. Meanwhile, a new transfer entropy (TE)-based causality analysis method is proposed for root cause diagnosis of quality-related faults. Finally, the whole proposed framework is practiced with real HSMP data, and the results demonstrate the usage and effectiveness of these approaches.