Monitoring, diagnostics and optimization are core challenges in the operation of technical systems. Valuable information on wear, causes of errors and potential for optimization are contained in the process data of machines and plants. However, this information is often left unused today because the amount and complexity of the data is too high (big data) and is not found by humans without technical support. Methods of machine learning and model-based anomaly detection have the potential for automated extraction of this information.
(1) Potential studies for plant operators: It is checked whether and what potential lies in the data of specific machines (prediction of errors / maintenance requirements, optimization of energy efficiency, increase cycle time / throughput / output, quality) and with which methods the information is made available can.
(2) Design of algorithms for analysis systems and for specific machines and plants.
(3) Test, development, demonstration and benchmarking of commercial big data analysis systems on real production facilities, in the lab 'Machine Learning' and the Smart Factory WLOW, an initiative of the Fraunhofer IOSB-INA and the OWL University of Applied Sciences.
This business unit focuses on the development and use of computer-based models to support complex automation systems. This includes:
Targeted support in specification, modeling, implementatoin, demonstration of
- OPC UA ClientServer/PubSub
- OPC UA information models
- Administrative shells and their submodels
- openAAS runtime environment