CIVIL-332 / 3 credits

Teacher: Fink Olga

Language: English


Summary

The course will cover the relevant steps of data-driven infrastructure condition monitoring, starting from data acquisition, going through the steps pre-processing of real data, feature engineering to developing suitable machine learning algorithms.

Content

Keywords

  • infrastructure condition monitoring
  • Feature engineering
  • signal processing
  • anomaly detection
  • machine learning

 

Learning Prerequisites

Required courses

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

Learning Outcomes

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

  • Assess / Evaluate the main challenges of collecting and processing real condition monitoring data
  • Apply different machine learning algorithms for anomaly detection
  • Apply classification-based machine learning algorithms for assessing the health condition
  • Interpret the results of the algorithms

Teaching methods

Lectures, excercises

Assessment methods

Performance will be assessed during the semester based on


-3 exercises, requiring the students to perform defined sub-tasks for designing Data Science approaches for infrastructure condition monitoring

 

Supervision

Office hours Yes
Assistants Yes
Forum Yes

In the programs

  • Semester: Spring
  • Exam form: During the semester (summer session)
  • Subject examined: Data Science for infrastructure condition monitoring
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 1 Hour(s) per week x 14 weeks

Reference week

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