Principal Investigator: Associate Professor Esmaeil S. Nadimi

Faculty of Engineering – Maersk Mc-Kinney Moller Institute – SDU Embodied Systems for Robotics and Learning- SDU

The research efforts we address with ABACUS 2.0 are related to data volume. The variety of sensor installed in wind turbines offer the unique opportunity to use large scale data to predict events altering the wind turbines’ performance. The problem at hand is the limited abilities of handling those data by local workstations. ABACUS 2.0 offers the platform to handle large scale data and perform analysis within reasonable time compared to human efforts. With ABACUS 2.0 we are turning various data types (times-series, logs, and other sensory data) into valuable insight, such as the remaining lifetime of turbines. Recent results have shown that learning from large scale wind turbine data can provide prediction horizons of several month prior to a failure of a wind turbine. In the future we want to utilize the parallel computing power to decrease the dimension of wind turbine sensor data into more informative data series. Ultimately obtaining knowledge about the underlying root cause for failures in wind turbines.