{"title":"Path detectability verification for time-dependent systems with application to flexible manufacturing systems","authors":"","doi":"10.1016/j.ins.2024.121404","DOIUrl":null,"url":null,"abstract":"<div><p>This paper addresses the path detectability verification problem for time-dependent systems modeled by time labeled Petri nets (TLPNs). To capture the information precisely, it may not be sufficient to estimate the current state by resorting to the partial system observation, and it is usually crucial to decide the path of a system to reach the current state. Path detectability characterizes a time-dependent system whose current state and the corresponding path can be uniquely determined after a real-time observation (RTO). Revised state class graphs (RSCGs) are proposed to capture the time information for the evolution of the RTO in a TLPN system. We demonstrate the time information overlap problem in the RSCG, i.e., several paths are associated with the same observable events and the same time instants, which leads to such paths that cannot be distinguished. The nodes required to be computed in the proposed RSCGs are always less or equal to those of the modified state class graphs reported in the literature, since the enumeration of all the states is avoided. Based on the RSCG, an RSCG observer is formulated to address the time information overlap problem and capture the number of such paths in the TLPN system. The efficiency analysis of this verification method is provided. In this paper, the results are applied to a real production system, exposing the practical value of the reported method.</p></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":null,"pages":null},"PeriodicalIF":8.1000,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information Sciences","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0020025524013185","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"0","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
This paper addresses the path detectability verification problem for time-dependent systems modeled by time labeled Petri nets (TLPNs). To capture the information precisely, it may not be sufficient to estimate the current state by resorting to the partial system observation, and it is usually crucial to decide the path of a system to reach the current state. Path detectability characterizes a time-dependent system whose current state and the corresponding path can be uniquely determined after a real-time observation (RTO). Revised state class graphs (RSCGs) are proposed to capture the time information for the evolution of the RTO in a TLPN system. We demonstrate the time information overlap problem in the RSCG, i.e., several paths are associated with the same observable events and the same time instants, which leads to such paths that cannot be distinguished. The nodes required to be computed in the proposed RSCGs are always less or equal to those of the modified state class graphs reported in the literature, since the enumeration of all the states is avoided. Based on the RSCG, an RSCG observer is formulated to address the time information overlap problem and capture the number of such paths in the TLPN system. The efficiency analysis of this verification method is provided. In this paper, the results are applied to a real production system, exposing the practical value of the reported method.
期刊介绍:
Informatics and Computer Science Intelligent Systems Applications is an esteemed international journal that focuses on publishing original and creative research findings in the field of information sciences. We also feature a limited number of timely tutorial and surveying contributions.
Our journal aims to cater to a diverse audience, including researchers, developers, managers, strategic planners, graduate students, and anyone interested in staying up-to-date with cutting-edge research in information science, knowledge engineering, and intelligent systems. While readers are expected to share a common interest in information science, they come from varying backgrounds such as engineering, mathematics, statistics, physics, computer science, cell biology, molecular biology, management science, cognitive science, neurobiology, behavioral sciences, and biochemistry.