递归定量分析在离心泵故障诊断中的运用

为了准确诊断离心泵的振动故障,针对振动信号的非平稳特征,提出了一种基于递归定量分析的离心泵振动故障诊断方法。采用递归定量分析(recurrence quantification analysis,简称RQA)方法提取离心泵振动信号的非线性特征参数,由这些特征参数构成特征向量,并以此作为改进Elman神经网络的输入,对神经网络进行训练,建立了离心泵运行状态分类器,用以诊断离心泵的不同状态。试验结果表明,递归定量分析与Elman神经网络相结合的方法可以准确诊断离心泵的振动故障。

Application of Recurrence Quantification Analysis to Fault Diagnosis of Centrifugal Pump

Abstract In order to diagnosis vibration fault of centrifugal pump accurately, aiming at the non-stationary characteristics of the vibration signals of centrifugal pump, a fault diagnosis method based on recurrence quantification analysis was put forward. First of all, the recurrence quantification analysis( RQA) method was used to extracted nonlinear characteristic parameter of the vibration signals, and the feature vector was generated by RQA nonlinear characteristic parameter. The feature vectors were employed as the input samples to train a modified Elman neural network, and then the running state classifier of the centrifugal pump was set up. The experimental results show that proposed method is effective for centrifugal pump fault diagnosis.