Modeling and Interpretation of Tidal Turbine Vibration Through Weighted Least Squares Regression

Tidal power is an emerging technology with great potential to provide a sustainable means of renewable energy in many areas worldwide. However, the nature of the underwater environment provides challenges. Submerged machinery cannot be easily accessed for inspections, and turbines must be brought to the surface for maintenance. This is an expensive process and results in prolonged periods of downtime where no power can be supplied to the grid. Condition monitoring systems, capable of accurately and remotely assessing the health state of machinery while in operation, can therefore be of great value to this industry. This paper presents an approach for condition monitoring of a tidal turbine's gearbox from monitoring data with low sample rates. Models of normal behavior were trained using weighted least squares regression, where prediction errors are used to identify changes in response. This paper then examines how prediction errors from a number of different cases (including changes in control scheme and simulated gearbox faults) can be interpreted by operators to classify anomalous behavior.