A Petri net model for railway bridge maintenance

Le, Bryant; Andrews, John; Fecarotti, Claudia

Abstract

This article describes the application of the Petri net modelling approach to managing the maintenance process of railway bridges. The Petri net model accounts for the degradation, inspection and repair processes of individual bridge elements in investigating the effectiveness of alternative maintenance strategies. The times governing the degradation and repair processes considered are stochastic and defined by the appropriate Weibull distribution. The model offers a capability for modelling the bridge asset which overcomes the limitations in the currently used modelling techniques reported in the literature. The bridge model also provides a means of predicting the future asset condition as a result of adopting different maintenance strategies. The solution of the Petri net model is performed using a Monte Carlo simulation routine. The application of the model to a typical metal railway bridge is also presented in the article.

Citation

Le, B., Andrews, J., & Fecarotti, C. (2017). A Petri net model for railway bridge maintenance. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 231(3), https://doi.org/10.1177/1748006X17701667

• Journal Article Type: Article
• Acceptance Date: Feb 26, 2017
• Online Publication Date: Apr 5, 2017
• Publication Date: Jun 1, 2017
• Deposit Date: Sep 15, 2017
• Publicly Available Date: Sep 15, 2017
• Journal: Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability
• Print ISSN: 1748-006X
• Electronic ISSN: 1748-0078
• Publisher: SAGE Publications
• Peer Reviewed: Peer Reviewed
• Volume: 231
• Issue: 3
• DOI: https://doi.org/10.1177/1748006X17701667
• Keywords: Bridge, asset management, degradation, lifetime analysis, Weibull distribution, Petri net, Monte Carlo
• Public URL: https://nottingham-repository.worktribe.com/output/863149
• Publisher URL: http://journals.sagepub.com/doi/10.1177/1748006X17701667
• Contract Date: Sep 15, 2017