化工学报2012，Vol.63Issue(2)：545-550,6.DOI:10.3969/j.issn.0438-1157.2012.02.029
基于EMD和边际谱频带能量的离心泵汽蚀故障诊断
Fault diagnosis of cavitation for centrifugal pump based on EMD and HHT marginal spectrum energy
摘要
Abstract
Centrifugal pumps are widely used in chemical processes. Cavitation in pumps is one of the major causes leading to reduced efficiency. A diagnostic method of cavitation inception was put forward based on low frequency and high frequency characteristics of pump inlet pressure fluctuation signals. The marginal spectrum was obtained 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 could be used for cavitation recognition. It took too much time to recognize when the characteristic dimension was high, therefore it was necessary to quantitatively analyze for simplication. A four-dimensional feature vector was put into the back propagation neural network for training and simulation, with the first and second level root-mean-square energy values of intrinsic mode function obtained through EMD as high frequency feature and the 0 to 20 Hz and 20 Hz to 40 Hz band energy values of marginal spectrum as low frequency feature. The method mentioned above increased the recognition rate by 7. 26% and 3. 59% with simulation time decreased by 77. 72% when contrasting with wavelet analysis method and EMD energy entropy method. It had a strong influence on the training of network that 3 to 9 level energy entropy of experimental data EMD varied little with cavitation conditions. So EMD energy entropy method took much time of simulation with low recognition rate. By removing the redundant characteristics recognition rate increased by 3. 59%, simulation time decreased by 77. 72%. The feature of wavelet analysis method was more varied in different flow rates, therefore recognition rate was lower for different flow rates.关键词
经验模态分解/Hilbert-Huang变换/边际谱/汽蚀/故障诊断Key words
empirical mode decomposition/ Hilbert-Huang transform/ marginal spectrum/ inception cavitation/ fault diagnosis分类
信息技术与安全科学引用本文复制引用
周云龙,刘永奇,薛广鑫,陈军..基于EMD和边际谱频带能量的离心泵汽蚀故障诊断[J].化工学报,2012,63(2):545-550,6.基金项目
吉林省教育厅"十一五"科学技术研究项目(200747). （200747）
