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Optimizing preventive maintenance policy: A data-driven application for a light rail braking system
Francesco Corman1, Sander Kraijema1,2, Milinko Godjevac1
1Department of Maritime and Transport Technology, Delft University of Technology, Delft, The Netherlands.
Proceedings of the Institution of Mechanical Engineers. Part O, Journal of Risk and Reliability
|December 27, 2017
Summary
Optimizing light rail maintenance reduces costs by 30% while maintaining reliability and availability. Data-driven models enable extended intervals and grouped actions for efficient preventive maintenance.
Area of Science:
- Engineering
- Operations Research
- Data Science
Background:
- Light rail rolling stock requires effective preventive maintenance policies.
- Current policies may not be cost-optimal or fully leverage system data.
- Reliability, availability, and maintenance costs are key performance indicators.
Purpose of the Study:
- To determine an optimal preventive maintenance policy for light rail braking systems.
- To reduce maintenance costs while ensuring system reliability and availability.
- To demonstrate the value of data-driven modeling in maintenance optimization.
Main Methods:
- Modeling system reliability degradation using Weibull distribution based on operational data.
- Analytically determining the relationship between reliability, availability, and maintenance costs.
- Employing a sequential optimization framework to adjust maintenance intervals.
Main Results:
- A data-driven model was developed and validated against existing system performance.
- Sensitivity analysis provided further insights into system behavior.
- The optimized policy achieved 30% maintenance cost reduction with equivalent availability and reliability.
Conclusions:
- Data-driven modeling is effective for optimizing preventive maintenance policies.
- Prolonging maintenance intervals and re-grouping actions can significantly reduce costs.
- The study provides a framework for improving light rail system efficiency.
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