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中文摘要：
提出一种基于最小二乘支持向量机（LS-SVM，least squares support vectormachine）理论的故障预测算法，利用该理论方法的回归预测算法进行分析，选用径向基核函数（RBF）作为预测模型核函数，通过MATLAB建模仿真，验证了对风力发电机组故障的较准确的实时预测。针对某风电场某台风机高速端轴承的振动数据进行特征参数预测研究，有效降低了模型的复杂度，且运算简单、精度高、收敛速度快。仿真结果表明，文中所提模型在预测精度和运算速度上都在很大程度上优于其他模型。
英文摘要：
By using least squares support vector machine （LS-SVM） and its regression prediction algorithm to analyze the theoretical approach, a wind turbine fault prediction system which is based on RBF kernel function as its prediction model is built. Matlab software is used to establish a forecasting model, and a more accurate real-time prediction of wind turbine faults is implemented. Based on the vibration data for a high-speed end bearing wind turbine and its characteristic parameters, the model is effectively reduced in complexity, and simple in operation, high in accuracy and fast in convergence. The results show that it is better than other models in both prediction accuracy and computing speed.
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