Scheduling preventive maintenance on a wind turbine based on quantitative data

This presentation focuses on the close relationship between statistical process control and preventive maintenance for a wind turbine. In particular, inspired by a physical model for the power output of the wind turbine, we design a statistical process monitoring procedure. The process is monitored with a control chart with the purpose of quickly detecting shifts to inferior operational states of the wind turbine due to the occurrence of unobservable assignable causes. At the same time, the information collected from the monitoring process is used to determine the overall operational state of the wind turbine, aka the degradation process of the asset. This degradation process moves in a continuous manner between two extremes (perfect condition and failure) with random measurement errors. Although, these three models, the physical model, the statistical model and the stochastic model, are obviously related, they have been typically treated in the literature independently. In this talk, we highlight the underlying connections between the three models and present a general mathematical model that can be used for the optimal identification of what constitutes sufficient evidence of imminent failure, so as to perform preventive maintenance, taking into account a maintenance cost structure.