101-0521-10L Machine Learning for Predictive Maintenance Applications
|Semester||Spring Semester 2020|
|Periodicity||yearly recurring course|
|Language of instruction||English|
|Comment||The number of participants in the course is limited to 25 students.|
Students interested in attending the lecture are requested to upload their transcript and a short motivation responding the following two questions (max. 200 words):
-How does this course fit to the other courses you have attended so far?
-How does the course support you in achieving your goal?
The following link can be used to upload the documents.
|Performance assessment information (valid until the course unit is held again)|
|Performance assessment as a semester course|
|ECTS credits||8 credits|
|Type||graded semester performance|
|Language of examination||English|
|Repetition||Repetition only possible after re-enrolling for the course unit.|
|Additional information on mode of examination||Performance will be assessed during the semester based on |
-5 exercises, requiring the students to perform defined sub-tasks for designing a predictive maintenance system (60% of the final grade in total)
-Presentation and reimplementation of a scientific journal paper recently published in the field of predictive maintenance (15%).
-Report (including the implementation) and presentation of a real case study of designing a predictive maintenance system based on raw condition monitoring signals of a complex engineered system (25%)