关键词: 钻井泵, 液力端, 故障诊断, 振动信号, 特征参数, 神经网络诊断系统

Abstract: The faults are resulted from some complex reasons in fluid end of drilling pump.The corresponding relationship between faults and symptoms is complex.In order to totally use information resources of vibration signal and to obtain more comprehensive and precise results,six amplitude-domain indexes such as Kurtosis,peak,pulsed,margin,wave and skewness,three frequency-domain parameters such as gravity,root mean squared and standard deviation,and 32 wavelet packet frequency band energy values were regarded as the reserve input feature vectors of the artificial neural network (ANN).An ANN diagnosis system was proposed based on synthesis feature parameters of vibration signals.In order to compare the property of network,the BP and RBF networks were established separately.The different combinations of the extracted vectors were taken as the input information of networks,and an optimal diagnosis system was obtained by diagnosis training and effective comparison.The actual tests prove that the ANN diagnosis system is effective and can get more accurate diagnosis rate in the fault diagnosis of fluid end in the reciprocating pump.

Key words: drilling pump, fluid end, fault diagnosis, vibration signal, feature parameter, artificial neural network diagnosis system