A Machine Vision and Electromagnetic Induction Synchronous Online Oil Monitoring Warning Method Using Multiscale Temporal Convolutional Network for Wind Turbine Gearbox

This article presents a wear condition warning method for wind turbine gearboxes. Initially, a diagnostic analysis was conducted to detect ferromagnetic wear particles larger than $4~\mu $ m using machine vision monitoring sensor and to identify metallic wear particles larger than $70~\mu $ m using electromagnetic induction sensor. The development of this synchronous monitoring method represents a pioneering application within the wind turbine gearboxes, with significant engineering implications. Subsequently, a machine vision-based approach was proposed to quantify wear conditions by establishing a wear index, incorporating parameters, such as the pixel area of wear particles and the structural similarity index metric (SSIM). To validate the effectiveness of this method, experimental investigations were conducted on a sample of five wind turbines. Ultimately, an integrated early warning system for monitoring the wear conditions of wind turbine gearboxes was proposed, incorporating the multiscale temporal convolutional network (MTCN). The MTCN model was evaluated using wear index data and electromagnetic induction sensor data from comparative experimental analyses. The results indicated that the MTCN model achieved a root mean square error (RMSE) of 1.1713, a mean absolute percentage error (MAPE) of 0.3416, and a coefficient of determination ( ${R} ^{{2}}$ ) of 0.8912, demonstrating superior performance compared to both the BiLSTM and BiGRU methodologies.