101-0521-10L  Machine Learning for Predictive Maintenance Applications

SemesterSpring Semester 2020
LecturersO. Fink
Periodicityyearly recurring course
Language of instructionEnglish
CommentThe 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.


101-0521-10 GMachine Learning for Predictive Maintenance Applications4 hrs
Tue09:45-11:30HIL C 10.2 »
10:00-12:00ER SA TZ »
Thu12:45-14:30HIL C 10.2 »
13:00-15:00ER SA TZ »
O. Fink

Catalogue data

AbstractThe course aims at developing machine learning algorithms that are able to use condition monitoring data efficiently and detect occurring faults in complex industrial assets, isolate their root cause and ultimately predict the remaining useful lifetime.
ObjectiveStudents will
- be able to understand the main challenges faced by predictive maintenance systems
- learn to extract relevant features from condition monitoring data
-learn to select appropriate machine learning algorithms for fault detection, diagnostics and prognostics
-learn to define the learning problem in way that allows its solution based on existing constrains such as lack of fault samples.
- learn to design end-to-end machine learning algorithms for fault detection and diagnostics
-be able to evaluate the performance of the applied algorithms.

At the end of the course, the students will be able to design data-driven predictive maintenance applications for complex engineered systems from raw condition monitoring data.
ContentEarly and reliable detection, isolation and prediction of faulty system conditions enables the operators to take recovery actions to prevent critical system failures and ensure a high level of availability and safety. This is particularly crucial for complex systems such as infrastructures, power plants and aircraft engines. Therefore, their system condition is increasingly tightly monitored by a large number of diverse condition monitoring sensors. With the increased availability of data on system condition on the one hand, and the increased complexity of explicit system physics-based models on the other hand, the application of data-driven approaches for predictive maintenance has been recently increasing.
This course provides insights and hands-on experience in selecting, designing, optimizing and evaluating machine learning algorithms to tackle the challenges faced by maintenance systems of complex engineered systems.

Specific topics include:

-Introduction to condition monitoring and predictive maintenance systems
-Feature extraction and selection methodology
-Machine learning algorithms for fault detection and fault isolation
-End-to-end learning architectures (including feature learning) for fault detection and fault isolation
-Unsupervised and semi-supervised learning algorithms for predictive maintenance
-Machine learning algorithms for prediction of the remaining useful life
-Performance evaluation
-Predictive maintenance systems at fleet level
-Domain adaptation for fault diagnostics
-Introduction to decision support systems for maintenance applications
Lecture notesSlides and other materials will be available online.
LiteratureRelevant scientific papers will be discussed in the course.
Prerequisites / NoticeStrong analytical skills.
Programming skills in python are strongly recommended.

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits8 credits
ExaminersO. Fink
Typegraded semester performance
Language of examinationEnglish
RepetitionRepetition only possible after re-enrolling for the course unit.
Additional information on mode of examinationPerformance 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%)

Learning materials

No public learning materials available.
Only public learning materials are listed.


No information on groups available.


PlacesLimited number of places. Special selection procedure.
Waiting listuntil 03.03.2020
End of registration periodRegistration only possible until 17.02.2020

Offered in

Civil Engineering MasterMajor in Construction and Maintenance ManagementWInformation
Mechanical Engineering MasterRobotics, Systems and ControlWInformation
Spatial Development and Infrastructure Systems MasterMajor Courses for all majorsWInformation
Robotics, Systems and Control MasterCore CoursesWInformation