Intelligent Monitoring of Engineering Systems
Key Info
Basic Information
- Degree:
- Master
- Semesters:
- Summersemester
- Lecturer:
- Univ.-Prof. Dr.-Ing. Bernd Markert
- Language:
- English
Further Information
Contact
Content
The elective course „Intelligent Monitoring of Engineering Systems“ links artificial intelligence / machine learning with the field of sensor-driven monitoring of engineering systems. The focus of the course is on practical group work, so the participants will apply the basics learned during the first half of the semester to solve a project task during the second one.
Topics
- Introduction: monitoring of engineering systems
- hands on: MATLAB
- scalars & vectors
- scripts
- plotting
- data import and export (*.csv, *.xls, *.ods, *.txt)
- sensing and data acquisition
- measurement chain
- sensors
- data acquisition & conversion processes
- signal processing
- continuous- & discrete-time signals
- (Fast) Fourier Analysis & Transform (FFT)
- window functions
- spectral leakage
- digital filters
- Machine Learning (ML)
- data preprocessing
- applications
- Non-destructive Testing (NDT)
- wave propagation
- Acoustic Emission (AE) analysis
- Tomography
- Thermography
- Structural Health Monitoring (SHM)
- elastic guided waves for damage detection
- condition monitoring of a CNC milling machine
Further information
Course Requirements
Programming skills in
- MATLAB
- (alt.: Python)
are helpful.
A MATLAB-hands-on is offered.
Course Objectives
The participants acquire basic knowledge in:
- Sensing
- Signal processing
- Machine learning
- Non-Destructive Testing (NDT)
- Structural Health Monitoring (SHM)
- Data pre- and postprocessing using MATLAB
Teaching and Learning Methods
The course curriculum consists of seminar lectures followed by a semester project. During the seminar lectures, the students will receive the necessary theoretical background to independently plan and execute the project in small groups. Consultation hours are offered to discuss challenges and problems arising during the course of the project. Finally, each group presents their achievements and results live and in form of a written report.
Accompanying literature
Nazarchuk, Z., Skalskyi, V., Serhiyenko, O., Acoustic Emission – Methodology and Application. Springer. 2017.
Farrar, C. R., Worden, K., Structural Health Monitoring: A Machine Learning Perspective. Wiley & Sons. 2013.
Goodfellow, I., Bengio, Y., Courville, A., Deep Learning. MIT Press. 2016.