摘要
由于风机轴承易发生故障且振动信号分析对于故障诊断极其有效,提出了基于自适应噪声完备集合经验模态分解(Complete Ensemble EmpiricalMode Decomposition with Adaptive Noise,CEEMDAN)和变分模态分解(Variational Modal Decomposition,VMD)相结合的信号处理方法。首先,使用CEEMDAN将采集到的振动信号分解成若干本征模态函数(Intrinsic Mode Function,IMF)分量,并使用能量加权合成峭度指标筛选故障特征明显的IMF分量,进行信号重构;之后,利用VMD将新的信号进行再分解,将VMD分解后每个IMF的能量比与基于包络熵和包络谱峭度组合的复合指标筛选出的最优IMF分量构建能量熵、样本熵、近似熵进行特征融合;最后,将融合特征矩阵输入到蛇优化算法(SO)优化支持向量机(SVM)进行识别和分类,实现多故障模式识别。通过仿真实验表明:此方法对于检测轴承十种劣化状态,诊断正确率达到98%。为风机轴承故障诊断提供了一种新的思路。
Since fan bearings are prone to failure and vibration signal analysis is extremely effective for fault diagnosis,this paper proposed a Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN)and Variational Modal Decomposition(VMD)combined signal processing method.Firstly,CEEMDAN was used to decompose the collected vibration signals into several Intrinsic Mode Function(IMF)components,and the energy-weighted composite kurtosis index was used to screen IMF components with obvious fault characteristics,and the signal was reconstructed.After that,the new signals were decomposed using VMD,and the energy ratio of each IMF after VMD decomposition was combined with the optimal IMF component screened by the composite index of envelope entropy and envelope spectrum kurtosis to construct energy entropy,sample entropy and approximate entropy for feature fusion.Finally,the fusion feature matrix was input into the Snake optimization algorithm(SO)optimization support vector machine(SVM)for recognition and classification,and multi-fault pattern recognition was realized.The simulation results show that the diagnostic accuracy of this method is 98%for the detection of ten kinds of bearing deterioration states.It provides a new way of fault diagnosis for fan bearing.
作者
王磊
刘国龙
杨磊
王志强
冯萌
姚学龙
包桦
张建盈
马向阳
WANG Lei;LIU Guolong;YANG Lei;WANG Zhiqiang;FENG Meng;YAO Xuelong;BAO Hua;ZHANG Jianying;MA Xiangyang(Ningxia Yinxing Energy Co.,LTD.,Yinchuan 750021,China)
出处
《微电机》
2024年第2期56-62,72,共8页
Micromotors
