基于MCKD和改进HHT多尺度模糊熵的齿轮故障诊断方法

Gear fault diagnosis method based on MCKD and multiscale fuzzy entropy of improved HHT

针对齿轮故障信号常伴有大量噪声,故障特征难以提取的问题,提出一种基于最大相关峭度解卷积(MCKD)和改进希尔伯特-黄变换(HHT)多尺度模糊熵的故障诊断方法。首先采用MCKD算法对采集到的齿轮振动信号进行降噪处理,以提高信号的信噪比;然后利用自适应白噪声完备经验模态分解(CEEMDAN)对降噪后信号进行分解,获得一系列不同尺度的固有模态函数(IMF),并通过相关系数-能量的虚假IMF评价方法选取对故障敏感的模态分量;最后计算敏感IMF分量的模糊熵,将获得的原信号多尺度的模糊熵作为状态特征参数输入最小二乘支持向量机(LS-SVM)中,对齿轮的故障类型进行诊断。实测信号的诊断结果表明,该方法可实现齿轮故障的有效诊断。

Aiming at the gear fault vibration signal often with lots of noise and fault feature of gear is weak,a gear fault diagnosis method based on maximum correlated kurtosis deconvolution(MCKD)and multiscale fuzzy entropy of improved Hilbert-Huang trasform is proposed.Firatly,the MCKD technique is used to eliminate the noise in the signal,to improve the signal noise ratio of the signal.Then,the signal is decomposed by complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN),obtain a series of intrinsic modal functions(IMFs)in different scales,and select the fault-sensitive modal component by the correlation coefficient-energy false IMF evaluation method.Finally,calculating the fuzzy entropy of sensitive IMF components,the obtained multiscale fuzzy entropy of the original signal is used as a state feature parameter input into the least squares support vector machine(LS-SVM)to diagnose the fault type of the gear.The results of a gear fault signals indicate that the proposed method can effectively diagnosis gear fault.