AI-driven Prognostics for Industrial Systems
This course is designed for engineers, scientists, operators, and managers interested in utilizing AI-based methods for condition monitoring and prognostics in industrial systems and high-value assets. Participants will learn to identify common failure causes and predict Remaining Useful Life (RUL) using historical data, involving tasks such as data processing, feature selection, model development, and uncertainty quantification. Led by experienced professionals from industry and academia, the course covers the basics of prognostics and introduces various AI methods, including deep learning. It represents state-of-the-art AI-driven prognostic techniques, advanced signal processing, and feature engineering methods.
Occasions for this course
-
Scope
3 credits
Time
2025-01-20 - 2025-03-30 (part time 25%)
Education level
Second cycle
Course type
Freestanding course
Application code
MDU-13052
Language
English
Study location
Independent of location
Teaching form
Distance learning
Number of mandatory occasions including examination: 0
Number of other physical occasions: 0Course syllabus & literature
See course plan and literature list (ERA324)Specific requirements
75 credits in energy engineering, mechanical engineering, production technology, product and process development, computer technology and/or computer science or equivalent or 40 credits in technology and at least 2 years of full-time professional experience from a relevant area within industry. In addition, English A/English 6 is required.
Selection
University credits
Spring semester 2026
-
Scope
3 credits
Time
2026-01-19 - 2026-03-29 (part time 25%)
Education level
Second cycle
Course type
Freestanding course
Application code
MDU-13052
Language
English
Study location
Independent of location
Teaching form
Distance learning
Number of mandatory occasions including examination: 0
Number of other physical occasions: 0Course syllabus & literature
See course plan and literature list (ERA324)Specific requirements
75 credits in energy engineering, mechanical engineering, production technology, product and process development, computer technology and/or computer science or equivalent or 40 credits in technology and at least 2 years of full-time professional experience from a relevant area within industry. In addition, English A/English 6 is required.
Selection
University credits
Questions about the course?
If you have any questions about the course, please contact the Course Coordinator.