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DCTRAIN: Data-driven predictive maintenance for rail-infrastructure

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Project Manager: Prof. dr. Paul Havinga

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The presently applied maintenance strategies in the railway sector are mainly based on past experience and periodic inspections. However, the inspections do not effectively prevent unexpected failures of the network, especially since degradation rates are rather unpredictable. As a result, many maintenance activities are performed in a reactive way, which yields network unavailability and relatively high maintenance costs. In a large research project, we now aim to transition from reactive maintenance to (pro-active) predictive maintenance. This will be achieved by developing and utilizing a range of continuous monitoring systems (data capturing) and the associated prediction models (data analytics) for failures of infrastructure elements (e.g. switches, joints, level crossings, track section) and by continuously improving failure mode, effects and criticality analyses (FMECAs) and root cause analyses (RCAs). This will significantly reduce the uncertainty on the actual condition of the network elements. The rail network already contains some sensors and there is a significant amount of data on usage, failures, maintenance activities and performance available. However, this data does not always contain the most relevant parameters and is not always processed in the right way, which means that the amount of useful information obtained from the data is rather limited.


The objective of the project is to develop data processing algorithms and associated diagnostic and prognostic methods for the different infrastructure elements, based on all data sources available in the field. This will be achieved by taking the following steps. Firstly, an overview of all available types of data must be created. Data will be available from traditional maintenance-related sources (failures, maintenance activities), but also additional sources could be identified: passengers and their mobile phones, satellites, sensors on measuring trains, regular trains and monitoring systems on the tracks. The second step will be to develop methods for collecting and merging all different data types. The third step, which constitutes the core of this project, is to develop data processing methods that  retrieve information on the actual state (diagnosis) or expected future failures (prognosis) of the railway elements. The ‘big data’ must be analyzed, where methodologies like machine learning, neural networks, data mining techniques, pattern recognition, etc. should be investigated. In a parallel PhD project within the same program, a physical model based approach will be followed, using the same railway elements and data sources. This will provide a unique opportunity to compare the two approaches. Moreover, the gained insight in the system and failure behavior also may guide the data mining, which makes it more than only a blind identification of the failures. On successful completion of the third step, the final step will be the application of the retrieved information in optimizing the maintenance process. If failure probabilities and life time distributions of critical elements can be quantified, these can be used as input for maintenance models, yielding decision support for the planning and clustering of maintenance tasks.


This project is part of a long-term collaboration between Strukton Rail Maintenance and the University of Twente.    

Project duration: 1 April 2015 - 1 April 2020

Participants: STRUKTON, UT

Project budget CTIT: 530 k-€ / 220 k-€ funding

Number of person/months CTIT: 1.2 fte/year

Involved groups: Pervasive Systems