泥浆泵液力端故障诊断技术研究

通过对现有的泥浆泵液力端故障诊断技术的分析研究，并结合泥浆泵的结构及工况特征，提出了振动信号统 计指标与神经网络相结合的液力端故障诊断方法。该方法选取振动信号的有效值、方差、峰值指标、脉冲指标、峭度指 标和裕度指标作为表征液力端振动信号的特征指标；采用动态数据采集仪、压电式加速度传感器采集振动测试信号， 并计算得出振动信号平均特征量；然后通过对振动信号特征指标的归一化处理，构建BP 网络和设置网络参数，将经 归一化处理后的时域统计指标作为训练样本，输入到构建的BP 网络中进行网络训练；经过训练，使BP 网络满足预定 的精度要求。现场应用诊断误差分别为：0.007 7，0.017 9，0.017 7，0.021 6，说明构建的BP 网络的性能能够满足故障 诊断要求。利用统计指标和BP 神经网络结合的故障诊断方法，对泥浆泵故障诊断具有较准确的识别效果，可应用于 泥浆泵液力端的故障诊断。

Study on Fault Diagnosis Technology for Fluid End of Drilling Pump

The fault diagnosis method of fluid end that combines the statistical indexes and neural network is proposed in this paper based on the analysis of fault diagnosis methods for fluid end of mud pump and the features of structure and operating condition of mud pump. Firstly，the following indexes of vibration signal are selected to illustrate the characteristic index： effective value，variance，peak index，impulsion index，kurtosis value and margin index，etc. At the same time，vibration testing signal is collected by using a dynamic data acquisition instrument and piezoelectric acceleration sensors and the average index for vibration signal is caculated. In order to construct the BP neural network and set network parameters，the selected characteristic indexes are normalized. As the training samples，then these indexes are put into the BP neural network for training， after which the constructed BP neural network can entirely meet the set training precision requirement. The corresponding diagnostic errors of different plungers in field are 0.007 7，0.017 9，0.017 7 and 0.021 6，which shows that the constructed BP network can reach the demands of plunger fault diagnosis. Therefore，this method may accurately diagnose fault of fluid end of mud pump，which can be applied to engineering practice.