Duration
26h Th, 26h Pr
Number of credits
Lecturer
Language(s) of instruction
English language
Organisation and examination
Teaching in the first semester, review in January
Schedule
Units courses prerequisite and corequisite
Prerequisite or corequisite units are presented within each program
Learning unit contents
The purpose of this course is to provide a broad understanding of the field of structural health monitoring with a particular focus on monitoring offshore structures and offshore wind turbines in particular. The course starts from defining and motivating SHM in the context of offshore wind energy, introduces the technologies that enable SHM, discusses key applications to sufficient depth to master the topic, touches on various other 'hot' topics including the digital twin and the use of Artificial intelligence.
The following topics are covered:
- General introduction to Structural Health Monitoring
- Data, Data Acquisition and sensors
- Structural dynamics and Operational Modal Analysis
- Load monitoring and Fatigue Life Assessment
- The Digital Twin
- The role of AI in SHM
- Various minor topics; e.g. Floating Wind, Corrosions, ....
- Workshop 1 : Hands on with sensors (pt1)
- Workshop 2 : Hands on with sensors (pt2)
- Workshop 3 : Operational modal analysis of OWT
- Workshop 4 : An introduction of handling fatigue data using python
- Workshop 5 : AI for SHM
Learning outcomes of the learning unit
At the end of the course, the student should:
- Be able to situate the role of SHM within the context of engineering and the offshore wind energy sector in particular
- Understand the sensor technology used in the field of SHM, including being able to motivate the choice for particular sensor solution and having some grip on the practical challenges associated with handling them
- Understand how to access, handle and process data in the context of SHM
- Understand how structural dynamics can be used to monitor structural health, and perform operational modal analysis to real world data using python.
- Understand the concept of load monitoring, and how to handle fatigue data using python
- Understand the potential role of a digital twin in structural health monitoring
- Have a grip on smaller topics, understanding the problems and challenges associated with them
- Understand the potential of AI in SHM, but become aware of the limitations and potential pitfalls. As well as using AI in python to handle SHM data.
Prerequisite knowledge and skills
To efficiently follow this course, it is important to have basic knowledge in various engineering topics and mathematics.
Planned learning activities and teaching methods
Mode of delivery (face to face, distance learning, hybrid learning)
Face-to-face course
Further information:
Lectures are mainly theoretical with the exception of 5 workshops held throughout the year.
The course is organized during the first semester over 12 weeks including: 7 lectures and 5 workshops. The planning of the course is presented during the first lecture. Room and timing can be found on CELCAT. Unless instructed otherwise.
Course materials and recommended or required readings
Platform(s) used for course materials:
- eCampus
Further information:
The course material are the slides and the jupyter notebooks during the workshops.
Exam(s) in session
Any session
- In-person
oral exam
Written work / report
Further information:
The exam in the first session is organized oral and comprises two parts;
- A part with written preparation (open book during preparation)
- A closed-book, oral examination covering the entire course, including the workshops
A single written workshop report, submitted two weeks after the final workshop. Covering all workshops, is submitted by every student. It accounts for 20% of the final grade.
Work placement(s)
Organisational remarks and main changes to the course
The lectures are taught by Prof. Weijtjens, who is a visiting professor and has no office at ULg. The workshops are organized with the support of external contributors from both industry and academia
All course material is posted weekly on e-campus.
Questions are best directed via mail.
Note that, depending on the sanitary situation, the course organization might need to be adapted.
Contacts
Students are encouraged to actively interact with the instructors, also outside of the lectures.