CIVIL-426 / 4 credits

Teacher: Fink Olga

Language: English


Summary

The 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.

Content

Keywords

machine learning; predictive maintenance, fault detection, fault diagnostics, fault prognostics

 

Learning Prerequisites

Required courses

Mandatory pre-requisite course: Introduction to machine learning for engineers or other machine learning courses

Recommended courses

 

 

Learning Outcomes

By the end of the course, the student must be able to:

  • Define the learning problem in way that allows its solution based on existing constrains such as lack of fault samples
  • Design data-driven predictive maintenance applications for complex engineered systems from raw condition monitoring data
  • Assess / Evaluate the performance of the applied algorithms
  • Choose machine learning algorithms for fault detection, diagnostics and prognostics
  • Interpret the results of the algorithms

Teaching methods

Lectures, excercises, final project

Assessment methods

Performance will be assessed during the semester based on
-4 exercises, requiring the students to perform defined sub-tasks for designing a predictive maintenance system (70% of the final grade in total)
-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 (30%)

Supervision

Office hours Yes
Assistants Yes
Forum Yes

In the programs

  • Semester: Fall
  • Exam form: During the semester (winter session)
  • Subject examined: Machine learning for predictive maintenance applications
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Semester: Fall
  • Exam form: During the semester (winter session)
  • Subject examined: Machine learning for predictive maintenance applications
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks

Reference week

 MoTuWeThFr
8-9  INM200  
9-10    
10-11     
11-12     
12-13     
13-14INM201    
14-15    
15-16     
16-17     
17-18     
18-19     
19-20     
20-21     
21-22     

Wednesday, 8h - 10h: Exercise, TP INM200

Monday, 13h - 15h: Lecture INM201