Industrial systems maintenance modelling using Petri nets

1999 ◽  
Vol 65 (2) ◽  
pp. 119-124 ◽  
Author(s):  
Zouakia Rochdi ◽  
Bouami Driss ◽  
Tkiouat Mohamed
Keyword(s):  
Petri Nets ◽  
Industrial Systems ◽  
Systems Maintenance

scholarly journals Supervisors of Petri Nets

2019 ◽  
Vol 27 (45) ◽  
pp. 33-41
Author(s):  
Jana Flochová ◽  
Tomáš Lojan
Keyword(s):  
Petri Nets ◽  
Communication Networks ◽  
Supervisory Control ◽  
Manufacturing Systems ◽  
Discrete Event Systems ◽  
Discrete Event ◽  
Modeling And Analysis ◽  
Industrial Systems ◽  
Software Application ◽  
Event Systems

Abstract The design and operation of modern industrial systems require modeling and analysis in order to select the optimal design alternative and operational policy. Discrete event system models are encountered in a variety of fields, for example computers, communication networks, manufacturing systems, sensors or actuators, faults diagnosis, robotics and traffic. The paper describes principles and methods of supervisory control of discrete event systems initiated by Ramadge and Wonham. Three supervisory control methods based on the Petri net models are introduced, and the key features of the Petri tool software application for the supervisory control of discrete event systems modeled by Petri nets are highlighted.

Maintenance cost assessment for heterogeneous multi-component systems incorporating perfect inspections and waiting time to maintenance

2021 ◽  
pp. 1748006X2110388
Author(s):  
Lucía Bautista ◽  
Inma T Castro ◽  
Luis Landesa
Keyword(s):  
Heuristic Algorithms ◽  
Cost Model ◽  
System Level ◽  
Maintenance Cost ◽  
Maintenance Policy ◽  
Industrial Systems ◽  
Inspection Strategy ◽  
Optimal Maintenance ◽  
Systems Maintenance ◽  
Safety Constraint

Most existing research about complex systems maintenance assumes they consist of the same type of components. However, systems can be assembled with heterogeneous components (for example degrading and non-degrading components) that require different maintenance actions. Since industrial systems become more and more complex, more research about the maintenance of systems with heterogeneous components is needed. For this reason, in this paper, a system consisting of two groups of components: degrading and non-degrading components is analyzed. The main novelty of this paper is the evaluation of a maintenance policy at system-level coordinating condition-based maintenance for the degrading components, delay time to the maintenance and an inspection strategy for this heterogeneous system. To that end, an analytic cost model is built using the semi-regenerative processes theory. Furthermore, a safety constraint related to the reliability of the degrading components is imposed. To find the optimal maintenance strategy, meta-heuristic algorithms are used.

Industrial Systems Maintenance under the Light of Reliability

2005 ◽  
Vol 5 (1) ◽  
pp. 13-17 ◽  
Author(s):  
Panagiotis Tsarouhas ◽  
Dimitrios Nazlis .
Keyword(s):  
Industrial Systems ◽  
Systems Maintenance

Maintenance Planning of an Offshore Wind Turbine Using Stochastic Petri Nets With Predicates

2013 ◽  
Author(s):  
Fernando P. dos Santos ◽  
Ângelo P. Teixeira ◽  
C. Guedes Soares
Keyword(s):  
Wind Turbine ◽  
Petri Nets ◽  
Stochastic Petri Nets ◽  
Offshore Wind ◽  
Offshore Wind Turbine ◽  
Industrial Systems ◽  
Offshore Wind Turbines ◽  
Component Systems ◽  
Generalized Stochastic Petri Nets ◽  
Reliability Block Diagrams

Operation and maintenance (O&M) activities have a significant impact on the energy cost for offshore wind turbines. Analytical methods such as reliability block diagrams and Markov processes along with simulation approaches have been widely used in planning and optimizing O&M actions in industrial systems. Generalized stochastic Petri Nets (GSPN) with predicates coupled with Monte Carlo simulation (MCS) are applied in this paper to model the planning of O&M activities of an offshore wind turbine. The merits of GSPN in modeling complex, multi-state and multi-component systems are addressed. Three maintenance categories classified according to the size and weight of the components to be replaced and the logistics involved, such as vessels, maintenance crew and spares and, the associated delays and costs are included in the model. The weather windows for accessing the wind turbine are also modeled. Corrective maintenance based on replacements and age imperfect preventive maintenance are modeled and compared in terms of the wind turbine’s performance (e.g. availability and loss production) and of the O&M costs.

Maintenance Planning of an Offshore Wind Turbine Using Stochastic Petri Nets With Predicates

2018 ◽  
Vol 140 (2) ◽  
Author(s):  
F. P. Santos ◽  
A. P. Teixeira ◽  
C. Guedes Soares
Keyword(s):  
Wind Turbine ◽  
Petri Nets ◽  
Stochastic Petri Nets ◽  
Offshore Wind ◽  
Offshore Wind Turbine ◽  
Industrial Systems ◽  
Offshore Wind Turbines ◽  
Operations And Maintenance ◽  
Generalized Stochastic Petri Nets ◽  
Maintenance Activities

Operations and maintenance activities have a significant impact on the energy cost for offshore wind turbines. Analytical methods such as reliability block diagrams and Markov processes along with simulation approaches have been widely used in planning and optimizing operations and maintenance actions in industrial systems. Generalized stochastic Petri nets (GSPNs) with predicates coupled with Monte Carlo simulation (MCS) are applied in this paper to model the planning of operations and maintenance activities of an offshore wind turbine. The merits of GSPN in modeling complex and multicomponent systems are addressed. Three maintenance categories classified according to the size and weight of the components to be replaced and the logistics involved, such as vessels, maintenance crew and spares and, the associated delays, and costs are included in the model. The weather windows for accessing the wind turbine are also modeled. Corrective maintenance (CM) based on replacements and age-dependent preventive maintenance (PM) with imperfect repair are modeled and compared in terms of the wind turbine's performance (e.g., availability and loss production) and of the operations and maintenance costs.

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