Determine the state of health of machines
Asset health and asset performance are essential for the efficient and trouble-free operation of technical systems such as machines, industrial plants, electrical devices, motor vehicles, and many more. Modern information systems continuously monitor technical systems to analyze and predict the state of health and performance.
The overall project goal is the design and analysis of robust learning algorithms for automated fault detection, fault diagnosis, and fault prediction. Robust learning works without complex system models. Robust algorithms learn the necessary model from available data. Because of their robustness, they can learn from data of poor quality such as data that contains itself faults. With the help of robust learning algorithms, we want to reduce the time necessary for manual system modelling. This automatization should enable new types of applications.
Software applications of learning-based fault detection, diagnosis & prediction are information systems for monitoring and analyzing technical systems. Foundations for these information systems are technologies of the “Internet of Things”. These are for example cheap and accurate sensors (example hardware) or geo-information systems (example software) such as their efficient interaction.
- Find more information regarding the AUTODETECT project here
- Duration: 01.01.2018 - 31.12.2020
- Partners: Software Competence Center Hagenberg GmbH (coordinator), ISW Industriesoftware GmbH
- Budget: ca. 490,000 € total costs and ca. 407,000 € funding contribution
- Funding Partner: Innovatives Oberösterreich 2020, Call "Digitalisierung"