Wind Turbine Generator Bearing Anomaly Detection and Explanation Using RRCF Approach.

Nowadays, machines are everywhere, and while their design and construction are a major challenge, maintenance has to be one of the main problems to solve during their life cycle. Reactive maintenance is simply waiting for a component to fail before taking action, which is not only costly but also leads to downtime and can result in lost revenue. Predictive maintenance, on the other hand, is proactive and can both minimise downtime and extend the life of the equipment. However, predictive maintenance relies on reliable forecasts to avoid over-scheduling. This problem is particularly interesting for wind farms, as they are usually located in remote areas, making access to repairs difficult and time-consuming. In this paper, we discuss predictive maintenance in the context of a wind farm and propose and develop a diagnostic and recommendation model based on the random-cut forest approach that can both assist an operator in his decision-making process and provide him with information to understand why he should make a particular decision. The methodology's effectiveness is verified and applied to a realm dataset of wind farms.