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An intelligent fault diagnosis method based on wavelet packer analysis and hybrid support vector machines

机译：基于小波包分析和混合支持向量机的智能故障诊断方法

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In this paper, a new intelligent method for the fault diagnosis of the rotating machinery is proposed based on wavelet packet analysis (WPA) and hybrid support machine (hybrid SVM). In fault diagnosis for mechanical systems, information about stability and mutability can be further acquired through WPA from original signal. The faulty vibration signals obtained from a rotating machinery are decomposed by WPA via Dmeyer wavelet. A new multi-class fault diagnosis algorithm based on 1-v-r SVM approach is proposed and applied to rotating machinery. The extracted features are applied to hybrid SVM for estimating fault type. Compared to conventional back-propagation network (BPN), the superiority of the hybrid SVM method is shown in the success of fault diagnosis. The test results of hybrid SVM demonstrate that the applying of energy criterion to vibration signals after WPA is a very powerful and reliable method and hence estimating fault type on rotating machinery accurately and quickly.
机译：提出了一种基于小波包分析（WPA）和混合支持机（Hybrid SVM）的旋转机械故障智能诊断方法。在机械系统的故障诊断中，可以通过WPA从原始信号中进一步获取有关稳定性和可变性的信息。 WPA通过Dmeyer小波分解从旋转机械获得的错误振动信号。提出了一种基于1-v-r SVM的多类故障诊断算法，并将其应用于旋转机械中。提取的特征将应用于混合SVM，以估计故障类型。与传统的反向传播网络（BPN）相比，混合SVM方法的优越性体现在故障诊断的成功上。混合支持向量机的测试结果表明，将能量准则应用于WPA后的振动信号是一种非常强大且可靠的方法，因此可以准确，快速地估计旋转机械上的故障类型。

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