Data Driven Fault Monitoring of Wind Farm Gearbox

As one of the key components of wind turbines, the gearbox usually has a high failure rate and long downtime due to long-term operation and operating conditions. This study aims to comprehensively consider various data such as temperature, wear, and stress, and adopt a fault monitoring technology based on neural network methods to improve the accuracy and reliability prediction of the operation status of wind power gearboxes. With the continuous accumulation of actual data, we can continuously optimize the parameters of neural network models, thereby achieving data-driven model optimization. This means that we can update and modify the model based on the new data collected during actual operation, so that it can better adapt to different working conditions and environmental changes. Ultimately, through this research method, we can more effectively manage the gearbox of wind farms, reduce downtime, optimize maintenance plans, and thereby improve the reliability and operational efficiency of the entire wind power system.