Data Science for infrastructure condition monitoring
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
Monitoring the condition of infrastructure systems is critical to assessing their health state and for deciding about the optimal mitigation and maintenance actions.
Different types of data can be collected on the infrastructure system condition, including vision-based data and (distributed) acoustic and vibration monitoring.
Data collected under real conditions is facing different challenges that need to be taken into account when pre-processing the data and developing the corresponding algorithms.
The course aims to cover the chain from data collection, pre-processing, feature engineering to developing machine learning algorithms that enable the detection of anomalous conditions.
Specific topics include:
-Condition monitoring data collection (including vision-based and (distributed) acoustic-based)
-Pre-processing of collected data
-Feature engineering (including feature extraction and feature selection)
-Machine learning algorithms for anomaly detection
-Classification-based machine learning algorithms
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
- 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
| Mo | Tu | We | Th | Fr | |
| 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 |
Légendes:
Lecture
Exercise, TP
Project, other