基于符号动力学信息熵与SVM的液压泵故障诊断
Fault Diagnosis of Hydraulic Pump Based on Symbolic Dynamics Entropy and SVM

针对泵车液压泵早期故障特征信号微弱、故障特征难以提取的问题，提出了一种基于符号动力学信息熵与支持向量机（support vector machine，简称SVM）的泵车液压泵故障诊断方法。分别模拟了液压泵9种故障状态，测取了各状态下多测点的振动信号样本值。利用时间序列的符号动力学信息熵，计算各振动信号的符号动力学信息熵Hk，确定了各状态下相应的信息熵特征向量。建立了不同状态特征向量训练集，再结合支持向量机对液压泵故障模式进行诊断与识别，测试结果准确率为98.71%。将该方法与改进的BP（back propagation，简称BP）神经网络诊断结果进行了对比，结果表明该方法的识别率更高，诊断时间更短，适用于现场液压泵故障的在线诊断。
In the light of the problem that the early fault feature signal of the hydraulic pump is weak and the fault feature is difficult to extract, a fault diagnosis method of hydraulic pump based on symbolic dynamics entropy and support vector machine(SVM) was proposed. Nine kinds of fault states of　hydraulic pump were simulated, and the sample values of the vibration signal of the multi measurement points were measured then, the symbolic dynamics entropy Hk of each vibration signal were calculated using time series symbolic dynamics entropy to determine the corresponding symbol dynamics entropy feature vector of each state. Finally, the training set of feature vectors in different status was established for SVM to diagnose and identify the fault state of hydraulic pump, the accuracy of the test was 98.71%.The comparison of the diagnostic results with the improved BP neural network showed that the method has a higher recognition rate of 98.71% and cost less time which helps in online diagnosis．