In order to increase prediction precision of SVR ( support vector regression) for catastrophic failures, an improved AdaBoost algorithm was proposed. It could obtain the weights of abnormal data in training sample set by AdaBoost algorithm and a weighted SVR was used to enhance the training of abnormal data, which could improve the prediction precision for catastrophic failure. The samples with small weight were discarded by using an adaptive weight trimming method to improve the training speed. The method was used to predict time series of an engine wear element and the one-step relative prediction error was 0. 025. The experiment results demonstrate that the method can improve the speed of fault prediction effectively under desired accuracy.%针对支持向量回归(SVR)方法对突变故障预测精度较低的问题,提出了一种改进的自适应增强算法(AdaBoost)提升SVR故障预测性能.该方法通过AdaBoost算法获取训练样本中突变点的权重并构造加权支持向量回归机增强突变点的训练,以提高对突变故障预测精度.利用自适应权重裁减方法剔除权重较小的样本点,来提高算法的训练速度.将本文方法用于发动机磨损元素的时间序列预测中,一步预测相对误差达到了0.025.实验结果表明该方法在保证预测精度的前提下有效地提高了故障预测速度.
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