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Vibration Fault Diagnosis Method of Centrifugal Pump Based on EMD Complexity Feature and Least Square Support Vector Machine

机译:基于EMD复杂性的离心泵振动故障诊断方法及最小二乘支持向量机

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Aiming at the non-stationary and non-linearity characteristics of the vibration signals of centrifugal pump, a new method based on complexity feature of Empirical Mode Decomposition (EMD) and Least Square Support Vector Machine (LS-SVM) is put forward. First of all, the Empirical Mode Decomposition (EMD) method was used to decompose the vibration signals into a finite number of stationary Intrinsic Mode Functions (IMF), and then complexity features of each IMF is extracted as the fault characteristics vectors and served as input parameters of LS-SVM classifier to diagnosis fault. Application results showed that the proposed method is very effective, which can better extract the nonlinear features of the fault and more exactly diagnosis fault.
机译:针对离心泵的振动信号的非静止和非线性特性,提出了一种基于经验模式分解(EMD)和最小二乘支持向量机(LS-SVM)的复杂性特征的新方法。首先,使用经验模式分解(EMD)方法来将振动信号分解为有限数量的固定内在模式功能(IMF),然后提取每个IMF的复杂性特征作为故障特性向量并用作输入LS-SVM分类器参数诊断故障。应用结果表明,该方法非常有效,可以更好地提取故障的非线性特征,更准确地诊断故障。

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