基于CV-GA-SVM方法的轴承故障诊断

为了有效地提取轴承的故障特征信号并进行准确的分类，采用在小波包变换中引入交叉验证遗传算法与支持向量机相结合的方法来识别故障轴承所发出的不稳定特征信号并进行诊断。首先，利用小波包变换的时-频化特征对瞬时变化的故障信号进行提取。然后，运用交叉验证遗传算法和支持向量机构建分类器对参数进行检测、优化和故障模式识别。最后，经实验来验证此算法的合理性。实验结果表明，此方法对于有限样本故障信号的检测和分类具有很高的准确性和可靠性、实时性。

Bearing Fault Diagnosis Based on CV-GA-SVM Approach

In order to effectively extract the fault characteristic signal of bearing and accurate classification, this paper uses the method of introducing the cross validation of genetic algorithm and support vector machine in combination with wavelet packet transformation, to identify the fault bearing issued by the unstable characteristic signal and diagnosis.Firstly, the fault signals of instantaneous changes using wavelet packet transform time-frequency characteristics are extracted. Then, using cross validation of genetic algorithm and support vector machine classifiers are built detection, optimization and fault pattern recognition of parameters. Finally, through the experiment to verify the rationality. The experimental results show that this method has real-time, high accuracy and reliability for the detection and classification of the finite sample fault signal.