【摘要】 由于往复压缩机气阀振动信号呈现强非线性和非平稳性特点,目前应用较好的三次样条EEMD(S-EEMD)方法仍然存在模态混叠及包络不准确问题。针对此情况提出一种基于四次Hermite插值EEMD(QH-EEMD)与功率谱熵(PSE)相结合的分析方法。结合四次Hermite插值保形性、可调性与EEMD提高信号在不同分解尺度上连续性的优点改善插值曲线的逼近精度,减少模态混叠,通过对振动信号进行分解,得到IMF分量。通过与S-EEMD-PSE(S-EEMD结合PSE)算法、QH-EEMD-SE(S-EEMD结合样本熵)算法比较,验证了QH-EEMD-PSE(QH-EEMD结合PSE)方法的优越性。以往复压缩机常见故障为研究对象,基于QH-EEMD-PSE方法提取故障特征实现了常见故障的准确诊断。更多还原

【Abstract】 Due to strong nonlinear and non-stationary characteristics of a reciprocating compressor’s valve vibration signals,the cubic spline interpolation EEMD(S-EEMD)method well utilized still has shortages of mode mixing and envelop inaccurate.Aiming at the above mentioned problems,the combined analysis method of EEMD based on the quartic Hermite interpolation(QH-EEMD)and the power spectral entropy(PSE)was proposed.The original signals were decomposed into a set of IMF components using the quartic Hermite method with advantages of shape-preserving and adjustability and the EEMD method promoting signals’ continuity in different decomposing scale to improve the approximation accuracy of the interpolation curve and to decrease mode mixing.The advantages of the QH-EEMD with PSE(QH-EEMD with PSE)analysis method were verified comparing with those of the S-EEMD-PSE(S-EEMD with PSE)method and the QH-EEMD-SE(QH-EEMD with sample entropy)method.Taking common faults of a reciprocating compressor as the study objects,feature vectors of faults were extracted based on the QH-EEMD-PSE method and the faults were diagnosed accurately.更多还原