Abstract: Through the lecture, the students acquire highly up-to-date knowledge about the current and future condition monitoring (CM) systems and their significance for the safety and service life of machines and structures. In addition, the students acquire practice-relevant skills and thus are independently able:
•to make a suitable selection of sensors and hardware for simple tasks in machine and structure diagnosis,
•to draw conclusions about the health and load state of the machine/structure from the vibrationbehavior,
•to interpret measurement data using signal analysis and feature extraction techniques (Matlab exercises),
•to program simple algorithms for automatic CM of machines and structures (Matlab exercises).
Content:
•Introduction (Examples of Machine and Structural Health Monitoring, CM and maintenance strate-gies, Overview of CM principles and methods)
•Sensors and Hardware for vibration-based CM (Sensor principles, Strain measurement, Measure-ment of speed and angles, Acceleration measurement, Hardware chain and sources of error, Sen-sor positions w.r.t. ISO standards)
•Signal processing for vibration-based CM (Characteristics of vibration signals, Characteristic signals from structural parts and rotating machine components, Principles of CM for rotating machinery, Principles of Structural Health Monitoring, Statistical-based change detection, Signal features fordamage identification, Machine learning for compensation of operational effects on signal features, Sensor fault detection within CM-Systems)
•Outlook: CM perspectives in a "digital world” (CM for networked machines in context of industry 4.0, Methods synthesis within CM strategies)
•Application cases for CM
- Dozent/in: Peter Kraemer
- Dozent/in: Marcel Wiemann