PhD on Post-Prognostics analysis for decision making in the context of condition based maintenance (CBM) of aerospace structures
Updated: 14 Nov 2020
We are looking for an ambitious PhD candidate to join the PYTHIA team: Artificial Intelligence for Structures, Prognostics and Health Management, part of the Structural Integrity & Composites Group.
Condition based maintenance is foreseen to be the standard approach to monitor the performance of an engineering asset, assess its health state, predict its remaining useful life, all in real-time, and plan maintenance actions accordingly. While research efforts have been focused on developing methodologies for prognostics, less emphasis has been given into post-prognostics analysis for decision making in the context of condition based maintenance. When is the optimum time to repair or to decomission the asset and how prognostics take future decision into account? The answer to these questions should take into consideration several parameters such as the RUL estimation uncertainties and the decision horizon duration, and it is beyond than just an optimization problem. The main challenge, whithin this PhD thesis, is to design a post-prognostics methodology that will enable an educated decision making process and at the same time will be integrated into the Diagnostics, Prognostics and Health Management (DPHM) framework, already developed by our team, enhancing the health management module.
This research includes active participation within the MORPHO European H2020 project (kick-off 01.04.2021).
The candidate should have a master degree in Electrical Engineering and/or Computer science with solid background on probability theory and statistics, machine learning and stochastic modeling.
Advanced programming skills, i.e. Python, C++, are required and experience in software architecture and end user programming is advantegeous. Candidates with a master degree in Aeronautics/Aerospace or Mechanical Engineering, who have worked with probability theory and machine learning during their Master thesis project, are welcome to apply.
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36 - 40 hours per week