A Multiple Wear Sensors Online Monitoring and Warning Method in Lubricating Oil Using Multidimensional Transformer Network for Wind Turbine Gearboxes

In this article, we presented a method for online oil monitoring and wear condition warning within the wind turbine gearboxes. Initially, an innovative wear particles sensor was engineered, utilizing a combination of electromagnetic and permanent magnet hybrid excitation, enabling the detection of ferromagnetic particles with diameters greater than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$40~\mu $ </tex-math></inline-formula>m. The precision of this sensor was thoroughly confirmed through a series of meticulous experimental analysis. Subsequently, the sensor was strategically positioned within a bypass circuit of the gearbox, in conjunction with electromagnetic induction and machine vision sensors, to establish an encompassing and coordinate monitoring framework. Finally, a multidimensional transformer network (Md-Transformer) was developed to integrate and process real-time sensor data for diagnostic analysis. From the data analysis of the two units selected from the engineering site for the past three years, the accuracy of root mean square error (RMSE) analysis was found to be 0.0002 and 0.0013; the accuracy of mean absolute percentage error (MAPE) analysis were 0.1831 and 0.1527; the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${R}^{{2}}$ </tex-math></inline-formula> (coefficient of determination) stability analysis were 0.8912 and 0.9002; the accuracy of multisensor fusion fault analysis was increased to 83.33%; and the Md-Transformer consistently outperformed established models such as nonlinear autoregressive model (NAR), gated recurrent unit (GRU), and temporal convolutional networks (TCNs), underscoring its robustness and efficacy in the surveillance of wind turbine gearbox health.