Data form the basis for use cases such as condition monitoring, diagnosis, predictive maintenance and optimization. Valuable information on wear, causes of faults and optimization potentials are contained in the process data of machines and plants. However, this information often remains unused today because the quantity and complexity of the data is too high and cannot be found by humans without technical support.
We develop solutions for the easy acquisition, management, visualization and analysis of large industrial data sets. For the analysis of the data, methods of machine learning and artificial intelligence are used to train behavioral models. The goal of the models is an automated extraction of information from process data.