Evidence fusion-based alarm system design considering coarse and fine changes of process variable. (May 2022)

• Record Type: Journal Article
• Authors: Weng, Xu; Xu, Xiaobin; Chang, Leilei; Hou, Pingzhi; Wang, Guodong; Dustdar, Schahram
• Is Part Of: Journal of process control. Volume 113(2022)
• Page Start: 68
• Page End: 79
• Publication Date: 2022-05
• Subjects: Dempster–Shafer theory of evidence -- Industrial alarm system -- Evidence fusion -- Data segmentation; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2022.03.007
• Languages: English
• ISSNs: 0959-1524

Abstract

Abstract: In view of the coarse and fine changes of process variable in industrial systems, this paper introduces a univariate alarm design method based on dynamic evidence fusion. Firstly, in order to describe the coarse statistical characteristics of historical sample data, the multi-transition data segmentation based on memory and forgetting strategies and the referential evidential matrix (REM) construction are presented. Secondly, the real-time sample of process variable is transformed into alarm evidence by matching with REM, and then such multiple pieces of alarm evidence continuously acquired in time are fused by evidence reasoning (ER) rule with the interval-valued fusion weights and reliabilities of alarm evidence, so as to accurately adapt the fine change of process variable. Finally, numerical experiment and motor rotor alarm experiment are implemented to validate that the proposed method has better performances than traditional alarm design methods. Highlights: Present the data preprocessing via data segmentation and REM to describe the coarse change of process variable. Exhibit the alarm design method via evidence fusion to adapt the fine change of process variable. Study on numerical and practical examples to show the advantage of the proposed method.