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中文摘要：
提出一种基于Voherra级数和支持向量机的旋转机械故障诊断方法。该方法首先利用量子粒子群优化算法辨识出正常、转子碰摩、转子裂纹、基座松动四种状态下的Volterra核，分别利用一阶Voherra核和前三阶Voherra核作为特征向量，然后将这些特征向量输入到SVM（support vector machine）分类器中进行识别。实验结果表明，提出的方法是有效的，当利用一阶Voherra核作为特征向量难以区分故障时，可以利用更高阶的Volterra核作为特征向量来区别，这些体现出所提出方法在旋转机械故障诊断中独特的优势。
英文摘要：
A new fault diagnosis method of rotating machinery based on Voherra series and support vector machine （SVM） is proposed. In the proposed method, the Volterra kernels are identified in the four conditions, i.e. normal, rotor crack, rotor rub, and pedestal looseness, by the quantum particle swarm optimization （QPSO） algorithm. Then the first order Voherra kernels and front three order Voherra kernels are respectively input into the SVM classifier for training. The experiment result shows that the proposed method is effective. When the type of fault is hardly distinguished with the first order Volterra kernels, the higher-order Volterra kernels can be used for classification. The proposed method has obvious predominance in the fault diagnosis of rotating machine.
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