基于快速谱峭度图的EEMD内禀模态分量选取方法
Feature Extraction Method of Intrinsic Mode Function in EEMD Based on Fast Kurtogram in Machinery Fault Diagnosis

针对在总体平均经验模式分解(ensemble empirical mode decomposition，简称EEMD)的多个内禀模态分量(intrinsic mode function，简称IMF)中，如何选取出反应故障特征的敏感IMF的问题，提出一种基于快速谱峭度图的敏感IMF选取方法。由EEMD分解获得的一组模式混淆的IMF，计算原信及各个IMF的快速谱峭度图，选择每个快速谱峭度图中谱峭度最大值所处的频带作为参考频带，比较各个IMF的参考频带与原信号谱峭度最大值所处频带之间的从属关系，筛选出反应故障特征的敏感IMF，为后续故障诊断提供特征信息。将该方法应用于模拟仿真信号及滚动轴承滚动体故障信号，验证了方法的有效性。
A complicated signal can be decomposed into a series of functions (IMFs) by ensemble empirical mode decomposition (EEMD). Since some IMFs are closely related to the faults while others are irrelevant, the method for selecting sensitive IMFs remains a problem. In light of this problem, a novel feature extraction method of IMFs based on fast kurtogram is proposed. Firstly, the fast computation of the kurtogram is used to calculate the kurtosis distribution of the original signal and the kurtosis distribution of each IMF in the frequency domain. Then, the reference frequency bands of the above-mentioned signals are determined by the maximum kurtosis band. By comparing the relationship between the reference frequency bands of the original signal and each IMF, the sensitive IMFs can be found. Finally, accurate information about the fault will be obtained for subsequent fault diagnosis processing. Both the simulated and real signals verify the effectiveness of the proposed method.