Challenges from Industrial Data Analytics

Big data applications in industry pose a number of unique challenges, setting them apart from domains such as consumer analytics in the web. Central for many industrial applications is time series data generated by often hundreds or thousands of sensors at a high rate, e.g. by a turbine. Another important data source are log files generated by control units in complex technical equipment, e.g. PLCs (programmable logic controller). This data can be used for failure statistics, root cause analysis, predictive maintenance, or for optimizing the performance during product design. Especially interesting are use cases that combine in-situ streaming analytics inside the local devices with centralized information, e.g. time series data collected from a whole fleet of wind turbines. In this talk I will describe a number of SiemensâĂŹ machine learning applications, especially failure diagnostics at the CERN Large Hadron Collider, self-optimizing wind turbines, and levee monitoring for Waternet Amsterdam. I will also discuss architectural challenges for such systems from a Big Data point of view.