Model-based Estimation of Remaining Useful Life

Lifelong Learning Modules - DTU

1-3 Months

Lifelong Learning Modules

Course Overview

This course equips wind engineers with practical, data-supported methodologies and tools to estimate the remaining useful life (RUL) of existing assets. By combining numerical models with historical operational data, this data-driven procedure creates an estimation of the actual consumed fatigue lifetime of an asset and supports informed lifecycle decisions. To enable participants to gain hands-on experience, the coursework leverages outputs from DTU Wind software tools, where open-access tools are provided to the participants, and results from proprietary tools (or data-driven surrogate model versions of them) are also provided.

Main Goal

To be updated

Skills to be Gained

After this course, you can:

  • Apply uncertainty quantification and propagation techniques using surrogate models.
  • Process and analyse SCADA data for wind turbine operations.
  • Use methodologies to recover fatigue load history from historical data, employing digital twins and virtual sensing techniques.
  • Identify relevant end-of-life decisions for wind assets and assess their feasibility based on economic and regulatory criteria.

Practical Notes

This course will be stackable with other LLL courses that will be developed by DTU Wind, to form certain specialisations or micro-degrees. For instance, we offer a series of courses that can be combined to form the specialisation “Data-Driven Decision Making for Wind Farm Operations.” This specialisation includes four courses, with “Model-Based Estimation of Remaining Useful Life” serving as the entry point.

Date:

To be updated

Period:

Expected duration:

1-3 Months

Format:

Online

Level:

To be updated

Language of instruction:

English

Requirements:

1. Solid understanding of the wind energy sector and wind turbine design or engineering processes, typically gained through a master’s degree in wind energy or equivalent industry experience.

2. Familiarity with Python programming.

Teaching and assessment methods:

The course follows a project-based learning format, where participants work with a real load model or dataset from an existing wind turbine, a relevant SCADA dataset, and regulatory information for a specific location. The program begins with lectures and tool demonstrations – including an introduction to the problem – followed by guided project work. In the final stage, participants present their results and justify their chosen methodologies. Both synchronous and asynchronous teaching methods are used.

Registration Price:

To be updated

Registration deadline:

To be updated

Instructors

Nikolay Dimitrov, Moritz Gräfe, Bruno Faria, Asger Abrahamse

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