부산대학교 도서관

针对自适应局部迭代滤波(Adaptive Local Iterative Filtering,ALIF)方法的模态混叠问题,提出了基于伪极值点的自适应局部迭代滤波(Pseudo-extrema-based Adaptive Local Iterative Filtering,PEALIF)方法.此方法采用增加伪极值点的方式使得信号极值点的分布更均匀,有效地抑制模态混叠问题的同时,亦保证了算法分解的顺序性.详细介绍了 EPALIF方法的原理,同时构建仿真信号,将此方法与EMD、EEMD、CEEMD和ALIF方法进行分析和对比.结果表明PEALIF在分解能力、抑制模态混叠和抗噪声干扰等方面都具有一定的优越性.最后,将此方法应用在双半内圈轴承故障诊断中,实验结果表明PEALIF方法能获取更突出且易于辨识的故障特征信息,证实了该方法应用在轴承故障诊断分析上的实用性.
Aiming at the modal aliasing problem of the Adaptive Local Iterative Filtering(ALIF)method,a Pseudo-extrema-based Adaptive Local Iterative Filtering(PEALIF)method is proposed,which uses the method of adding pseudo-extrema to make the distribution of signal extremely more uniform,effectively suppressing the prob-lem of modal aliasing,and also ensuring the order of algorithm decomposition.The principle of the PEALIF method is introduced in detail.Simultaneously,simulation signals are constructed,and this method is analyzed and com-pared with the Empirical Mode Decomposition(EMD),Ensemble Empirical Mode Decomposition(EEMD),Complementary Ensemble Empirical Mode Decomposition(CEEMD)and ALIF methods.The results show that PEALIF has certain advantages in decomposition ability,suppression of modal aliasing and anti-noise interference.Finally,this method is applied to the fault diagnosis of double inner ring bearing.The experimental results show that the PEALIF method can obtain more prominent and easily identifiable fault feature information,which confirms the application of this method in the bearing fault diagnosis analysis.