Abstract
Prognostics and health management (PHM) technologies permit actionable information to enable proper decision-making for improving systems’ performance. With the increasing requirements placed on the rail systems’ availability, better maintenance decisions should be evaluated before practical application. The aim of this work is to build maintenance models and estimate the performance of considered maintenance decisions regarding the rail system’s reliability and availability by means of Colored Petri nets. As a high-level formalization method, Colored Petri nets provide different color sets, which are suitable to represent different maintenance attributions. The maintenance models are evaluated at both the structure and parameterization levels. At the structure level, the structure correctness of the maintenance models is evaluated by using the state space analysis. At the parameterization level, specific maintenance decisions are illustrated. With various maintenance parameters, comparisons of system reliability and availability are made with the results obtained with the Colored Petri nets model.
Highlights
IntroductionThe maintenance influences the reliability and availability of the system [3]
Maintenance plays an essential role in a system’s life cycle
While this paper focuses on the analysis of system maintenance and availability by means of Colored Petri nets (CPN), which validates the correctness of system structure and estimates the performance of considered maintenance decision in an overall system level
Summary
The maintenance influences the reliability and availability of the system [3]. Achieving a high maintainability in the railway system requires a proper maintenance strategy. More maintenance means more life-cycle cost, while it may not lead to a dramatical improvement in the reliability. As in a free market, the optimal maintenance strategy can guarantee the availability of railway system and have the best economic benefits. For the system maintenance and availability analysis, there are mathematical formulating and model-based analysis approaches. Garmabaki et al presented the Multi-Attribute Utility Theory (MAUT), which used multiple objective functions to evaluate the cost and reliability of the maintenance optimization [5]. All in all, comparing with the mathematical formulating approach, the model-based analysis can provide a more structured overview of the system. It is much easier to read than the pure mathematical calculation [14]
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