Three kinds of coprognosability for partially-observed discrete event systems via a matrix approach. (November 2021)

• Record Type: Journal Article
• Authors: Zhou, Yingrui; Chen, Zengqiang; Liu, Zhongxin; Zhang, Zhipeng
• Is Part Of: Nonlinear analysis. Volume 42(2021)
• Publication Date: 2021-11
• Subjects: Security and safety -- Decentralized fault prognosis -- Coprognosability -- Semi-tensor product -- Cyber–physical systems -- Partially-observed discrete event systems; Nonlinear functional analysis -- Periodicals; Analyse fonctionnelle non linéaire -- Périodiques; Nonlinear functional analysis; Periodicals; 515.7248
• DOI: 10.1016/j.nahs.2021.101073
• Languages: English
• ISSNs: 1751-570X

Abstract

Abstract: Due to the important application in security and safety analysis, fault prognosis of discrete event systems (DESs) plays a more vital role in study of Cyber–physical systems. In this work, we study the problem of decentralized fault prognosis in the context of partially-observed DESs. Different from existing results, we establish algebraic structures of given systems under decentralized agents by semi-tensor product rather than observers or verifiers based on formal language methods. The structure matrices of decentralized partially-observed DESs are polynomial in the size of a given system and linear about the number of agents. Besides, combining the formal method and algebraic state space approach, we discuss three kinds of coprognosability, called ( M, K ) -disjunctive-coprognosability aiming at disjunctive architectures, ( M, K ) -conjunctive-coprognosability for conjunctive architectures and ( M, K ) -strongly-coprognosability focusing on general structures, respectively. Here, ( M, K ) is the performance bound of a given prognostic system. In order to take both fault prediction and isolation into consideration, the decentralized prognoser is required to issue the " j " alarm for the j th type of fault and " 0 " means no fault alarm. Meanwhile, we propose a polynomial-time algorithm to verify each kind of coprognosability based on the structure matrix approach and show that each verification is not separate.