Coarse-to-fine condition identification for wide-range non-stationary processes driven by coupled condition indicators. (November 2022)

• Record Type: Journal Article
• Authors: Zheng, Jiale; Chen, Xu; Zhao, Chunhui
• Is Part Of: Control engineering practice. Volume 128(2022)
• Publication Date: 2022-11
• Subjects: Wide-range non-stationarity -- Couple condition indicators (CIs) -- The coarse-to-fine condition identification (CFCI) strategy -- Spatial and temporal correlations -- Process monitoring; Automatic control -- Periodicals; 629.89
• DOI: 10.1016/j.conengprac.2022.105328
• Languages: English
• ISSNs: 0967-0661

Abstract

Abstract: Frequent changes in operating conditions would result in wide-range non-stationarity and bring significant challenges to tracking data characteristics for industrial processes. Commonly, those changes are closely related to some coupled condition indicators (CIs). Therefore, this paper explores the synergy of coupled CIs on process characteristics and proposes a coarse-to-fine condition identification (CFCI) strategy to identify condition modes for process monitoring. Firstly, the coarse-level identification probes into the distribution of augmented CIs using the density-based clustering algorithm and then restores the time-wise non-stationary data into condition-wise new analysis units. Secondly, the fine-level identification is performed with canonical variate analysis (CVA) on new analysis units to obtain fine-scale condition modes by exploring the influences of the coupled CIs on process characteristics. In this way, process data can be automatically divided into fine-grained condition modes with the concurrent evaluation of CIs and the changing law of process characteristics under the synergy of coupled CIs. Specifically, the CFCI strategy naturally builds the links between process data and coupled CIs, which also provides explicit physical interpretations for the recognized condition modes. Finally, local models resorting to CVA are rationally constructed to present comprehensive monitoring of different condition modes. The validity of the proposed method has been empirically demonstrated against other related methods through an industrial process.