Research interests

In the Section of Railway Engineering, I work towards practical and fundamental issues in the field of Structural Condition Monitoring and Maintenance of Railways. This field includes monitoring, modeling, analysis and control of railway systems, with a focus on the whole system (the infrastructure and its dynamic interactions with trains, maintenance actions, infra-managers, railway companies, rail passengers, society and environment). The main objective in this research is to develop new systematic and computationally efficient monitoring, modeling and decision support methods for railway systems, combining knowledge of the physical assets (track, power supply, stations, vehicles, communications, etc.) together with new advances in signal processing and control for railway data sources. The research field also includes a real-implementation component, considering the design of proper benchmarks, real-data acquisition, measurements in the railway tracks, and interaction with practitioners at the different authorities (NS, ProRail, and contractors), consultancy firms, and policy makers.

Regarding the fundamental component, I work on general aspects of health condition monitoring and maintenance decision support systems, with methodologies coming from the artificial intelligence field, machine learning, Big Data analytics, together with different non-centralized and centralized model predictive control structures.




Research output

  1. Prediction interval methodology based on fuzzy numbers and its extension to fuzzy systems and neural networks

    Research output: Contribution to journalArticleScientificpeer-review

  2. Distributed chance-constrained model predictive control for condition-based maintenance planning for railway infrastructures

    Research output: Chapter in Book/Report/Conference proceedingChapterScientific

  3. Entropy-Based Local Irregularity Detection for High-Speed Railway Catenaries With Frequent Inspections

    Research output: Contribution to journalArticleScientificpeer-review

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