Server Failure Detection for Robustness Analysis in Automated Manufacturing Systems With Arbitrary Resource Failures

In the paradigm of Petri nets (PNs), this paper focuses on the maximally permissive robustness analysis for automated manufacturing systems (AMSs) with arbitrary resource failures through server failure detection. First, unreliable system of sequential systems with shared resources (U-S<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup>Rs) are constructed by adding detection subnets to the original PNs for each unreliable place. Based on different failed statuses of unreliable resources, the reachable marking set of a U-S<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup>R is partitioned into one safe and multiple failed subsets, where there exists a one-to-one relationship between all markings in the safe subset and those of the original system. Second, subnet systems corresponding to part types are categorized to provide the definitions of the robust, strongly robust, and weakly robust markings. Third, we provide algorithms to compute the sets of strongly connected, weakly connected, and dangerous markings in each failed zone. When resources are in a failed status <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">X</i><sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>f</i></sub>, we establish an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">X</i><sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>f</i></sub>-failure-dependent marking set. The robustness of all markings within that set can be evaluated by examining the connectedness of the markings derived from those in that set. Moreover, for all markings not in the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">X</i><sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>f</i></sub>-failure-dependent marking set but in the safe subset, we provide conditions further to determine the robustness of these markings. Finally, examples are presented to illustrate the proposed methods and show the advantage over the existing ones.

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Publication Details

Journal
IEEE Transactions on Automation Science and Engineering
Published
2025-01-01
DOI
https://doi.org/10.1109/tase.2025.3528996
Citations
2
Primary Topic
Fault Detection and Control Systems
Type
article
Field-Weighted Citation Impact
2.43

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article

Server Failure Detection for Robustness Analysis in Automated Manufacturing Systems With Arbitrary Resource Failures

Hesuan Hu, Junyan Li
2 citations
IEEE Transactions on Automation Science and Engineering
Fault Detection and Control Systems
2.43
article

Server Failure Detection for Robustness Analysis in Automated Manufacturing Systems With Arbitrary Resource Failures

Hesuan Hu, Junyan Li
article en
2 citations

Abstract

In the paradigm of Petri nets (PNs), this paper focuses on the maximally permissive robustness analysis for automated manufacturing systems (AMSs) with arbitrary resource failures through server failure detection. First, unreliable system of sequential systems with shared resources (U-S<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup>Rs) are constructed by adding detection subnets to the original PNs for each unreliable place. Based on different failed statuses of unreliable resources, the reachable marking set of a U-S<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup>R is partitioned into one safe and multiple failed subsets, where there exists a one-to-one relationship between all markings in the safe subset and those of the original system. Second, subnet systems corresponding to part types are categorized to provide the definitions of the robust, strongly robust, and weakly robust markings. Third, we provide algorithms to compute the sets of strongly connected, weakly connected, and dangerous markings in each failed zone. When resources are in a failed status <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">X</i><sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>f</i></sub>, we establish an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">X</i><sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>f</i></sub>-failure-dependent marking set. The robustness of all markings within that set can be evaluated by examining the connectedness of the markings derived from those in that set. Moreover, for all markings not in the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">X</i><sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>f</i></sub>-failure-dependent marking set but in the safe subset, we provide conditions further to determine the robustness of these markings. Finally, examples are presented to illustrate the proposed methods and show the advantage over the existing ones.

IEEE Transactions on Automation Science and EngineeringVol. 22
Xidian University (CN)
National Natural Science Foundation of China
Decent work and economic growth
Openalex Percentile: Top 14%
Fault Detection and Control Systems
2.43
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