Research on Performance Prediction Model of Wind Turbine Gearbox Lubricating Oil Based on Deep Learning (应用人工智能研究, 2026)
Gearbox lubricating oil oxidation degradation severely impairs the operational stability of wind turbines and increases maintenance costs. Conventional detection methods (e.g., Rotating Pressure Vessel Oxidation Test (RPVOT), Pressure Differential Scanning Calorimetry (PDSC)) have high instrument dependence and long testing cycles, and may not meet on-site rapid detection demands. This study took in-service industrial gear oil samples as the research object, selected five physicochemical indices as input variables, and systematically optimizes data preprocessing (Standard Normal Variate (SNV)), feature extraction (Principal Component Analysis (PCA)), and machine learning algorithms (Back Propagation Neural Network (BPNN)/Support Vector Machine (SVM)/Random Forest (RF)). The proposed SNV-PCA-BP hybrid model achieved excellent predictive performance with a R2 of 0.9960 and Residual Prediction Deviation (RPD) of 6.1247, which is 360 times more efficient (defined as single-sample detection time) than traditional RPVOT/PDSC methods based on parallel tests of 62 samples. This model provides a low-cost and reliable technical support for the predictive maintenance of wind turbine gearboxes.