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The density of railway traffic has been steadily increasing over past years and decades. The developments have implicated a growing need for efficient operation and maintenance of railway rolling stock systems. Also the increased operation of articulated trains has induced new challenges on maintenance organization and planning. Selecting optimal maintenance strategies for each component does not only influence the availability of the railway vehicles but also the operational performance and the profitability of the operator. Suitable tools to analyse, compare and optimize different maintenance strategies are therefore required. Petri nets are such a mathematical tool that and have been applied for maintenance modeling and simulations of different applications. Several types of Petri nets with different properties have been introduced. One of the recently proposed extensions of Petri nets are the Abridged Petri Nets (APN) which fulfill the specific requirements of railway rolling stock maintenance. In this paper, we propose the application of APN in combination with the Monte-Carlo simulation for railway rolling stock maintenance evaluation. In a first step, the applicability of the APN approach was demonstrated on a theoretical case study comprising a condition based maintenance strategy for a system. In a second case study, several real application case studies were modeled and compared based on the processes and real application field data of three railway vehicle components. The tool can be further extended by pre-defining selected strategies that be easily implemented within an overall decision support system.
Eisenberger et al. (Sun,) studied this question.