CAJ | 학술논문

剖析液压系统故障特征，采用了一种双相关分析（D-CA）和改进的集合经验模态分解（R-EEMD）相结合的液压系统故障提取新方法。该方法首先对原信号进行自相关分析，突出信号中的周期成分和去噪，利用支持向量回归机（SVR）延拓来改进的EEMD对原信号的自相关函数进行分解；得到理论意义上的固有模态函数（IMF）。再通过求取IMF分量与自相关处理的信号的频域而非传统时域上的互相关系数，去除虚假IMF分量。最后对去除虚假分量重构信号进行Hilbert谱分析提取信号的故障特征。该方法提高了信噪比，减少了IMF的数量，抑制了端点效应，成功地提取了液压系统故障特征频率。
부석액압계통고장특정，채용료일충쌍상관분석（D-CA）화개진적집합경험모태분해（R-EEMD）상결합적액압계통고장제취신방법。해방법수선대원신호진행자상관분석，돌출신호중적주기성분화거조，이용지지향량회귀궤（SVR）연탁래개진적EEMD대원신호적자상관함수진행분해；득도이론의의상적고유모태함수（IMF）。재통과구취IMF분량여자상관처리적신호적빈역이비전통시역상적호상관계수，거제허가IMF분량。최후대거제허가분량중구신호진행Hilbert보분석제취신호적고장특정。해방법제고료신조비，감소료IMF적수량，억제료단점효응，성공지제취료액압계통고장특정빈솔。
A new method for hydraulic-system fault-feature extraction was proposed based on double-correlation analy-sis (D-CA) and refined ensemble empirical-mode-decomposition (R-EEMD). Firstly, the original signal was processed by adaptive correlation analysis to extrude the periodic components and eliminate the noise. Secondly, the R-EEMD based on support vector regression (SVR) was used to analyze the adaptive correlation processing signal to obtain the theoretical in-trinsic mode function (IMF). Thirdly, the false IMF components were removed by extracting the correlation coefficient of IMFs and the adaptive correlation processing signal in the frequency domain instead of traditional time domain. Finally, the reconstructed signal was analyzed by the Hilbert spectrum to extract the fault features. Simulation and experimental results show that this method can increase signal-to-noise ratio, reduce the number of IMFs, depress the end effect, and effectively extract the faults feature frequencies of the hydraulic system.