Diagnosis of Time Petri Nets Using Fault Diagnosis Graph

Wang, Xu; Mahulea, Cristian; Silva, M.

doi:10.1109/tac.2015.2405293

Cited by 29 publications

(17 citation statements)

References 37 publications

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“…However, it fails to explicitly show how to determine the transition confidence coefficient, leaving the system with a large number of uncertainties. The forward-backward algorithm was used to implement reasoning pertaining to unobservable place events in the model [17]. Many other fault diagnosis methods, such as data fusion and the support vector machine (SVM), were proposed as well.…”

Section: Complexitymentioning

confidence: 99%

Sensor Fault Diagnosis Based on Fuzzy Neural Petri Net

Xiao-lin

Cheng

2018

Complexity

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This study aims to improve the operating stability of the resistance strain weighing sensor and eliminate fuzzy factors in fault diagnosis. Based on fuzzy techniques for fault diagnosis, the proposed fuzzy Petri net model uses the fault logical relationship between a sensor and an improved Petri net model. A formula for confidence-based reasoning is proposed using an algorithm, which combines neural network regulation algorithm with a transition-enabled ignition judgment matrix. This formula can yield an accurate assessment of the operating state of the sensor. Backward inference and the minimum cut set theory are also combined to obtain the priority of faults, which helps avoid blind and ambiguous maintenance. The sensor model was analyzed, and its accuracy and validity were verified through statistical analysis and comparison with other methods of fault diagnosis.

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Section: Complexitymentioning

confidence: 99%

Sensor Fault Diagnosis Based on Fuzzy Neural Petri Net

Xiao-lin

Cheng

2018

Complexity

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“…For timed DESs, Wang et al. proposed an online approach for fault diagnosis of a system modeled by a Time PN (TPN) with partially observed transitions. The faults are assimilated to a subset of unobservable transitions.…”

Section: Related Workmentioning

confidence: 99%

“…The diagnosis of PNs by the state estimation is based on markings and/or firing sequences estimation for fault detection and localization. For timed DESs, Wang et al [1] proposed an online approach for fault diagnosis of a system modeled by a Time PN (TPN) with partially observed transitions. The faults are assimilated to a subset of unobservable transitions.…”

Section: Related Workmentioning

confidence: 99%

Diagnosis of Partially Observed Petri Net Based on Analytical Redundancy Relationships

Chouchane

Khedher

Nasri

et al. 2018

Asian Journal of Control

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In this paper, we design an efficient diagnosis technique for partially observed discrete event systems modeled by labeled Petri nets. The fault detection is based on analytical redundancy relationships derived from the nominal model. The decomposition of the Tun‐induced subnet to connected subgraphs allows determining the subgraphs that may contain faults. To appreciate the fault localization, a set of analytical redundancy relationships is etablished for each fault transition based on the fault model. The proposed diagnosis approach is independent of the length of the observed sequence and independent of the number of unobservable transitions. The detected faults with the proposed approach are faults which led to a change in the number of tokens in the net.

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“…More recently, Basile et al (2015) extend the previously mentioned approach developed in Basile et al (2013) to deal with the state estimation and the fault diagnosis problem for systems modelled by time PN augmented with labels. Wang et al (2015) thanks to a fault diagnosis graph (FDG) which is a truncation of the conventional SCG (Berthomieu and Diaz, 1991) developed an online technique for the fault diagnosis of systems modelled by unlabelled time PNs. The FDG is constructed incrementally with respect to the current observation and its number of states can be, in the worst case, the same as the one of the traditional SCG.…”

Section: Literature Reviewmentioning

confidence: 99%

Diagnosability analysis and fault diagnosis of P-time labelled Petri nets

Bonhomme

2018

IJCCBS

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This paper addresses the problem of analysing the diagnosability of a P-time labelled Petri net with partial information. Indeed, the set of transitions is partitioned into those labelled with the empty string ε called silent (as their firing cannot be detected) including the faulty transitions and the observable ones. The diagnosability can be defined as the ability to detect the type of a failure within a finite number of steps after its occurrence -the system is then said to be diagnosable. The proposed approach is based on the synthesis of a modified state observer where the fault transitions are considered as observable allowing the construction of a sampath-like diagnoser. The novelty of the developed approach resides in the fact that, although the time factor is considered as intervals, the diagnoser is computed thanks to the underlying untimed Petri net structure of the P-time labelled model considered. Furthermore, the method relies on linear programming techniques and the schedulability analysis of particular firing sequences exhibited by the analysis of the obtained diagnoser and does not require the building of the state class graph.

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Diagnosis of Time Petri Nets Using Fault Diagnosis Graph

Cited by 29 publications

References 37 publications

Sensor Fault Diagnosis Based on Fuzzy Neural Petri Net

Sensor Fault Diagnosis Based on Fuzzy Neural Petri Net

Diagnosis of Partially Observed Petri Net Based on Analytical Redundancy Relationships

Diagnosability analysis and fault diagnosis of P-time labelled Petri nets

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