Abstract: Centrifugal pumps are widely used in chemical production. Cavitation happened in pumps is one of the major causes which leading to the reduction of efficiency. A diagnostic method of inception cavitation is put forward based on low frequency and high frequency characteristics of pump inlet pressure fluctuation signals. The marginal spectrum is getted through empirical mode decomposition (EMD) of experimental data and Hilbert-Huang transform (HHT). By qualitative analysis root-mean-square and marginal spectrum band energy of each intrinsic mode function can be used for cavitation recognition. But it takes too much time to recognize when the characteristic dimension is high, therefore it is necessary to quantitatively analyze to simplify characters. A four-dimensional feature vector was put into the back propagation neural (BP) networks for training and simulation , with the first and the second level root-mean-square energy value of intrinsic mode function get through EMD as high frequency feature and the 0 to 20 Hz and 20 to 40 Hz band energy values of marginal spectrum as low frequency feature. The method mentioned above has increased the recognition rate by 7.26% and 3.59% with simulation time decreasing by 77.72% when contrasting with wavelet analysis method and EMD energy entropy method. It has a strong influence on the training of networks that 3 to 9 level energy entropy of experimental data EMD varies little with cavitation conditions. So EMD energy entropy method takes much time of simulation with low recognition rate. Removing the redundant characteristics recognition rate increased by 3.59, simulation time decreased by 77.72% . During wavelet analysis characteristic vector selected varies much with flow rate, therefore recognition rate is high for the same flow rate and is low for different flow rate. For characteristic value in this paper varies not much and similarly with flow rate, recognition rate raises by 7.26% .

Key words: empirical mode decomposition, HilbertHuang transform, marginal spectrum, inception cavitation, fault diagnosis

摘要： 离心泵在化工生产中得到广泛应用。汽蚀是导致离心泵效率降低的主要原因之一。快速准确的诊断出汽蚀故障，具有重要意义。提出一种基于泵入口压力脉动信号低频和高频特征的汽蚀故障诊断方法。以经验模态分解产生的本征模态函数的均方根能量值作为高频特征，以希尔伯特—黄变换的边际谱频带能量值作为低频特征，组成4维特征向量输入BP网络训练、仿真。通过对比小波分析法和EMD能量熵方法，表明本文提出的方法能够快速、更准确的诊断汽蚀故障。

关键词: 经验模态分解, HilbertHuang变换, 边际谱, 汽蚀, 故障诊断