基于DE-SVM的风电机组高速轴承故障诊断

为减少风电机组传动链故障造成的重大损失和安全隐患，保证机组健康平稳运行，针对风电机组传动链轴承的故障诊断问题，提出一种基于差分进化算法改进支持向量机的故障诊断方法。利用集合经验模态分解方法对原始数据进行处理，提取有效的故障特征，实现信噪分离；采用差分进化算法对支持向量机关键参数进行优化以提高模型的泛化能力和预测精度；利用训练好的模型进行故障诊断。结果表明：所提方法具有准确性和有效性。

Fault Diagnosis of Wind Turbine High Speed Bearing Based on DE-SVM

In order to reduce the major losses and potential safety hazards caused by the failure of the wind turbine drive chain and ensure the healthy and stable operation of the unit, a fault diagnosis method based on SVM improved by difference evolution(DE) algorithm was proposed for the fault diagnosis of the wind turbine. The original data were processed by using EEMD method to extract effective fault features and realize signal-to-noise separation; the key parameters of SVM were optimized by using difference evolution algorithm to improve the generalization ability and prediction accuracy of the model; the trained model was used for fault diagnosis. The results show that the proposed method is accurate and effective.