Industrial systems maintenance modelling using Petri nets

Rochdi, Zouakia; Bouami, Driss; Tkiouat, Mohamed

doi:10.1016/s0951-8320(98)00093-3

Cited by 29 publications

(15 citation statements)

References 4 publications

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“…Stochastic Petri Networks (SPN) (Dutuit et al, 1997), (Nourelfath and Dutuit, 2004) are now considered as a traditional method to model reliability, availability, etc. SPN are used in the domain of dynamic reliability (Volovoi, 2004) and the maintenance policy optimization (Zouakia et al, 1999). This method is a powerful modeling formalism but unfortunately the reliability analysis is based on a simulation procedure.…”

Section: Stochastic Petri Network (Spn)mentioning

confidence: 99%

Overview on Bayesian networks applications for dependability, risk analysis and maintenance areas

Weber¹,

Medina-Oliva²,

Simon³

et al. 2012

Engineering Applications of Artificial Intelligence

608

285

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International audienceIn this paper, a bibliographical review over the last decade is presented on the application of Bayesian networks to dependability, risk analysis and maintenance. It is shown an increasing trend of the literature related to these domains. This trend is due to the benefits that Bayesian networks provide in contrast with other classical methods of dependability analysis such as Markov Chains, Fault Trees and Petri Nets. Some of these benefits are the capability to model complex systems, to make predictions as well as diagnostics, to compute exactly the occurrence probability of an event, to update the calculations according to evidences, to represent multimodal variables and to help modeling user-friendly by a graphical and compact approach. This review is based on an extraction of 200 specific references in dependability, risk analysis and maintenance applications among a data base with 7000 Bayesian Network references. The most representatives are presented, then discussed and some perspectives of work are provided

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Section: Stochastic Petri Network (Spn)mentioning

confidence: 99%

Overview on Bayesian networks applications for dependability, risk analysis and maintenance areas

Weber¹,

Medina-Oliva²,

Simon³

et al. 2012

Engineering Applications of Artificial Intelligence

608

285

View full text Add to dashboard Cite

show abstract

“…Other works focus on computation of the underlying Markov chain [35] or on the steady-state simulation analysis [36], [37]. Rochdi et al [38] presented an algorithm to compute the MCS of a coherent FT by means of transforming it into a PN, and computing its reachability set. In [30], system reliability is analyzed using reliability block diagrams instead of FTs, and a conversion algorithm to colored PNs [39] is also presented.…”

Section: Related Workmentioning

confidence: 99%

On Qualitative Analysis of Fault Trees Using Structurally Persistent Nets

Rodríguez

2016

IEEE Trans. Syst. Man Cybern, Syst.

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A fault tree (FT) defines an undesired top event, characterizing it using logic combinations of lower-level undesired events. In this paper, we focus on coherent FTs, i.e., the logic is restricted to AND/OR formulas. FT analysis is used to identify and assess the minimal cut sets (MCSs) of an FT, which define the minimal set of events leading to the undesired state. The dual of MCS is minimal path set (MPS). MCS and MPS are commonly used for qualitative evaluation of FTs in safety and reliability engineering. This paper explores computation of the MCS/MPS of an FT by means of structural analysis (namely, computation of minimal p-semiflows) of a Petri net (PN) that represents the FT. To this end, we propose a formal definition of a coherent FT and a transformation from this model to a PN subclass (namely, structurally persistent nets). We also prove the relationship between minimal p-semiflows and MCS/MPS in an FT. In addition, we propose an algorithm that uses linear programming techniques to compute the MCS/MPS in an FT. Finally, we put our findings into practice by qualitatively evaluating the FT of a pressure tank system.

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“…The co mmon-cause failure of components 0 and 1 is described by the transition t 1. If the transition t 1 is fired, then it removes a token each from p laces P 0 , P 1 and P 19 deposit a token each in the places P 14 and P 18 . The common-cause repair action is depicted by the transition t 19.…”

Section: Gspn Specificationmentioning

confidence: 99%

Generalized Stochastic Petri Nets for Reliability Analysis of Lube Oil System with Common-Cause Failures

Thangamani¹

2012

AJCAM

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A very high level of availab ility is crucial to the economic operation of modern power plants, in view of the huge expenditure associated with their failures. Th is paper deals with the availab ility analysis of a Lube oil system used in a combined cycle power plant. The system is modeled as a Generalized Stochastic Petri Net (GSPN) taking into consideration of partial failures of their subsystems and common-cause failures; analyzed using Monte Carlo Simu lation approach. The major benefit of GSPN approach is hardware, software and human behavior can be modeled using the same language and hence more suitable to model co mplex system like power p lants. The superiority of this approach over others such as network, fault tree and Markov analysis are outlined. The numerical estimates of availability, failure criticality index o f various subsystems, co mponents causing unavailability of lube oil system are brought out. The proposed GSPN is a pro mising tool that can be conveniently used to model and analyze any co mple x systems.

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Industrial systems maintenance modelling using Petri nets

Cited by 29 publications

References 4 publications

Overview on Bayesian networks applications for dependability, risk analysis and maintenance areas

Overview on Bayesian networks applications for dependability, risk analysis and maintenance areas

On Qualitative Analysis of Fault Trees Using Structurally Persistent Nets

Generalized Stochastic Petri Nets for Reliability Analysis of Lube Oil System with Common-Cause Failures

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