针对风电机组齿轮箱油池温度异常难以早期预警的问题，提出一种基于数据采集与监视控制（supervisory control and data acquisition， SCADA）数据的故障预警方法，以提升机组运行可靠性。首先，结合风速–功率分布特征，采用四分位法与基于数据离散度的纵向滤波剔除异常功率点；随后，利用随机森林算法筛选影响油池温度的关键输入特征，构建类别提升算法（categorical boosting，CatBoost）温度预测模型，并采用树结构 Parzen 估计器（tree-structured parzen estimator，TPE）优化其超参数；最后，基于残差分布，通过统计过程控制确定动态预警阈值。在某风电场实际故障案例中，该模型在齿轮箱故障发生前约5 h发出有效预警，残差超出控制限的时间点与故障发展过程高度吻合。所提方法能有效识别油池温度异常工况，具备良好的早期预警能力与工程应用价值。

In response to the challenge of early warning for abnormal oil sump temperatures in wind turbine gearboxes， a fault warning method based on supervisory control and data acquisition （SCADA） data is proposed to enhance the operational reliability of the turbines. Firstly， by integrating wind speed-power distribution characteristics，an outlier detection approach utilizing the interquartile range and longitudinal filtering based on data dispersion is employed to eliminate anomalous power points. Subsequently， key input features influencing oil sump temperature are identified using a random forest algorithm， leading to the development of a temperature prediction model based on categorical boosting （CatBoost）. The hyperparameters of this model are optimized using tree-structured parzen estimator （TPE）. Finally， dynamic warning thresholds are established through statistical process control based on residual distributions. In a practical case study from a specific wind farm， this model issued effective warnings approximately 5 hours prior to gearbox failure； notably， the time points at which residuals exceeded control limits closely aligned with the progression of faults. The proposed method demonstrates significant efficacy in identifying abnormal conditions related to oil sump temperatures and possesses strong early warning capabilities along with substantial engineering application value.