Data-driven asset management
[Open soon]
Work description
The work is framed within the AI4WIND project and focuses on asset management associated with wind turbine blades, which are characterised by demanding operating conditions and high criticality in terms of availability and safety. The central objective is to develop a platform that helps reduce operation and maintenance costs and decrease unplanned downtime in wind farms. The project is also expected to support the extension of the useful life of wind energy assets, as well as a potential increase in energy production, the creation of new business models, and the strengthening of the export and internationalisation potential of the technologies developed. The work integrates the analysis of failure modes and structural degradation in wind turbine blades, the use of monitoring data from optical sensors, SCADA systems, and inspection reports, as well as the development of artificial intelligence models for early damage detection, prediction of degradation evolution, and estimation of the remaining useful life of the assets. Additionally, it includes the development of edge processing and edge–cloud communication methodologies, enabling complex data on deformation, loads, and structural condition to be transformed into reliable and actionable operational information. In particular, the aim is to assess the impact of these solutions on the overall performance of wind energy assets, including availability, annual energy production, operation and maintenance costs, risk of unplanned failures, and extension of blade lifetime. The project will thus contribute to the development of advanced capabilities in condition monitoring, predictive maintenance, decision support, and asset management in the context of renewable energy systems.
Academic Qualifications
Bachelor Degree in Industrial Engineering and Management or similar area.
Minimum profile required
Average grade of 18 in the Master Degree in Industrial Engineering and Management.Proficiency in Portuguese and English.
Preference factors
Experience in developing data-driven models for decision support, including the analysis of real-world data, modelling, and solution evaluation. Experience with quantitative methods applied to engineering problems (e.g., optimisation, simulation, statistical analysis, or applied machine learning).
Application Period
Since 06 Aug 2026 to 19 Aug 2026
[Open soon]
Centre
Industrial & Systems Engineering and Management