Analysis of Fault Diagnostic Technique in Wind Turbine Using Machine Leaning Models

Wind energy stands out as a key player in the renewable energy landscape, thanks to its cost-effective and low-carbon electricity generation methods. However, the reliability of wind turbines hinges on effective maintenance strategies. When turbines experience unscheduled downtime, it not only drives up Operation & Maintenance (O&M) costs but also impacts the overall reliability and availability of wind energy production. A major culprit behind these unexpected outages is the premature failure of rotating components. To tackle this issue, condition monitoring serves as a vital tool, continuously tracking turbine dynamics and identifying faults at their early stages. This proactive approach allows for the prediction of potential failures, enabling timely maintenance to prevent serious damage. In this research, we propose a fault diagnostic technique that utilizes adaptive time-space processing through vibration signal analysis, combined with a machine learning model. This method aims to classify common tribological faults in wind turbine drivetrains, such as scuffing, polishing, fretting corrosion, overheating, and assembly damage.