BASELINE-DIFFERENCING: A NOVEL APPROACH FOR BUILDING GENERALIZABLE OCEAN TURBINE RELIABILITY MODELS

Abstract

Ocean turbines are a promising new source of clean energy, but their remote and inhospitable environment (the open ocean) poses reliability challenges. Machine condition monitoring/prognostic health monitoring (MCM/PHM) systems assure the reliability of these turbines by detecting and predicting machine state. These MCM/PHM systems use sensor data (such as vibration information) to determine whether or not the machine is operating properly. However, not all sensor data corresponds to the machine state: some portions of the sensor signal are influenced by certain environmental conditions which do not directly relate to machine health. Therefore, models must be built which can detect system state regardless of these environmental operating conditions. The proposed baseline-differencing approach permits this by creating a baseline for different conditions (such that each baseline represents what the normal, healthy machine state looks like while in that operating condition) and using the difference of the observed data and this baseline to train and evaluate models. We present two case studies, both conducted on data from a dynamometer representing an ocean turbine, to demonstrate the improved predictive capabilities of models which incorporate baseline-differencing, compared to the models which use the nonbaselined data. The results show that significantly more high-quality models can be built with baseline-differencing.