In order to realize fault prediction of equipments,the paper uses the Support Vector Machine (SVM) as the basic learning algorithm,and adopts the weighted SVM regression method to assign relatively larger weight to the mutation point,so as to enhance the training of the mutation point and improve the fault prediction accuracy.By using the adaptive weight clipping method,the sample points with smaller weights are eliminated by calculating the regression weights of the sample points.Meanwhile,the number of samples participating in the training is reduced so as to improve the forecasting speed.Finally,the fault prediction model is established by selecting the appropriate kernel function and related parameters.The validity and superiority of the algorithm are verified by taking a certain type of communication station as an example.%为实现装备的故障预测,把支持向量机(SVM)作为基础学习算法,采用加权支持向量机回归方法,对突变点赋予较大的权值,增强对突变点的训练,提高故障预测精度.采用权重自适应裁剪方法,通过计算样本点的回归权重,剔除权重较小的样本点,减少每次参与训练的样本个数,提高预测速度.选定合适的核函数及相关参数,建立故障预测模型,研究以某通信电台为例,验证了算法的有效性和优越性.
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