Long title: Knowledge-based optimization of fleet-asset management

Assignment to our (IMW) research priorities:

  • Knowledge-based Maintenance
  • Data analysis
  • CRISP-DM
  • Predictive Modeling & Machine Learning

 

Duration: 01.10.2023 – 30.09.2027

Abstract:

The maintenance management of subway fleets currently relies solely on the analysis of failure factors and the expertise of experts. Consequently, the possibilities for continuous optimization of fleet assets are now exhausted. Balancing safety, reliability, and costs poses a challenge that hinders optimal progress. Within the scope of this project with Wiener Linien, sensor data and failure data from a specific system of a rail vehicle, the V-train, are to be utilized to provide meaningful information on maintenance metrics. Incorporating multimodal data facilitates data- and knowledge-driven maintenance, enabling life-cycle-dependent and dynamic optimization decisions for fleet deployment. Thus, at any point in the life cycle, the optimal and maximum possible safety, reliability, and cost metrics are intended to be simulated.

Results:

Exploration and development of a methodology for mapping and simulating the dependencies among vehicle data, faults, maintenance data, reliability, availability, maintainability, and supportability (RAMS), and life cycle costs (LCC) within a life-cycle-dependent, dynamic, and knowledge-based model. This model should be capable of simulating optimizations or changes in fleet deployment or fleet maintenance and their effects.

Partners:

Contact details:

Dipl.-Ing. Andreas Steiner

Email: andreas.steiner@tuwien.ac.at