Condition Monitoring, Data Analytics and Reliability Engineering

The digital transformation leads to technical systems with enhanced functionality. Modern technical systems are equipped with sensor networks, which augment the already available operating data and enable for a monitoring of the overall system. Condition monitoring often comprises diagnosis about the current condition and the prediction of future conditions of the technical system or the product quality in a production line. Condition Monitoring systems rely on data analytics to identify patterns and trends in data sets to create a benefit for the overall system. To do so, methods associated to Data Analytics and primarily approaches from machine learning are utilized in our projects. In our research, the focus is primarily on the diagnosis of the current health state and the prediction of the remaining useful lifetime of a technical system. A current research field is the development of robust analysis approaches for systems under non-stationary operating conditions for which a prediction of future system conditions is challenging due to the increased uncertainty. Another filed of research is the combination of a priori engineering knowledge about a technical system at hand and data-driven algorithms.

The development process of mechatronic systems focuses on different design objectives among them dependability, which comprises further aspects e.g. reliability, availability, safety and integrity. Self-optimization of technical systems enables for an autonomous behavior adaption as a reaction to a change in operating conditions and user demands. Based on self-optimization, methods to increase dependability during operation are developed, e.g. configuration control, active control of the reliability of the system or to form a Digital Twin for support of maintenance.