Abstract
This study proposes a fault diagnosis method of discrete event systems on the basis of a Petri net model with partially observable transitions. Assume that the structure of the Petri net model and the initial marking are known, and the faults can be modeled by its unobservable transitions. One of the contributions of this work is the use of the structure information of Petri net to construct an online fault diagnoser which can describe the system behavior of normal or potential faults. By modeling the flow of tokens in particular places that contain fault information, the variation of tokens in these places may be calculated. The outputs and inputs of these places are determined to be enabled or not through analyzing some special structures. With the structure information, traversing all the states is not required. Furthermore, the computational complexity of the polynomial allows the model to meet real-time requirements. Another contribution of this work is to simplify the subnet model ahead of conducting the diagnostic process with the use of reduction rules. By removing some nodes that do not contain the necessary diagnostic information, the memory cost can be reduced.
Highlights
Fault diagnosis is a critical issue in most industrial systems when preserving the safety of equipment and human operators. is issue has been studied in numerous studies that concern discrete event systems [1,2,3,4,5,6]
The fault diagnosis of discrete event system (DES) is discussed by several approaches based on models [7,8,9,10], such as automata models, which usually lead to constructing a diagnoser automaton
Any additional unobservable transition may be associated with system legal behavior. en, an algorithm decides whether the system behavior is normal or has potential faults when observed sequences occur, and special paths are defined to depict the structure information of Petri nets
Summary
Fault diagnosis is a critical issue in most industrial systems when preserving the safety of equipment and human operators. is issue has been studied in numerous studies that concern discrete event systems [1,2,3,4,5,6]. Fabre et al [27] proposed a net-unfolding approach to design an online asynchronous diagnoser that can avoid state explosion. For avoiding the state explosion problem, an online fault diagnosis strategy is proposed in this paper on the basis of the structure information of partially observed Petri nets. En, an algorithm decides whether the system behavior is normal or has potential faults when observed sequences occur, and special paths are defined to depict the structure information of Petri nets. E diagnostic algorithm in this paper is with great advantage for avoiding the state explosion especially when looking for a reasonably efficient method for online use with large intelligent systems. For the detailed discussion on Petri nets, refer to [28]
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