From April 2011 to February 2012
Sipke Hoekstra and Wienik Mulder
Maintenance costs for large capital goods, such as trains and airplanes, account for more than 60% of their total lifecycle costs. This illustrates the relevance of research in this area. To carry out maintenance at the right moment, an assessment of the condition is crucial. Typically this is based on calendar time, but usage parameters (startups, usage hours, km/miles etc.) can be better predictors. NedTrain maintains the rolling stock in The Netherlands. They recently installed a system to transmit data from the board computer of a train to the office where maintenance tasks are planned.
The goal of the master assignment was to develop a guideline for the effective monitoring of usage to contribute to the planning of preventive maintenance tasks. First of all a suitable usage parameter needs to be selected. Next an optimal trigger is set. Triggering too late will result in failures, but too early in over maintaining the asset. A case-study was made on the compressor of the train to illustrate and validate the developed guideline.
For the selection of the right usage parameter a proved connection with the failure behavior is essential. Historical data can be used to create a stochastic model to describe the failure behavior based on the selected usage parameter. The developed guideline was applied on the compressor of a train. The proposed alternative offers the potential to save a considerable amount of costs.
Keeping assets such as trains in good condition is not as natural as I thought. Technology plays an important role. In this project I enjoyed combining maintenance models/theory with practice. Furthermore, I had the freedom to decide for the direction of the research.