改进小波去噪Teager算子的齿轮微弱故障提取方法

针对齿轮箱在强噪声背景下齿轮微弱故障振动信号的特征不易被提取的问题,提出将改进小波去噪和Teager能量算子相结合的微弱故障特征提取方法。采用改进小波阈值函数对振动信号进行去噪处理,与形态学滤波和传统小波阈值函数相比能够有效地提高信号的信噪比。对去噪后的信号进行集合经验模态分解(ensemble empirical mode decomposition,简称EEMD)得到若干本征模式函数(intrinsic mode function,简称IMF),计算各IMF分量与原信号的相关系数并结合各IMF分量的频谱剔除虚假分量。对有效的IMF分量计算其Teager能量算子,并重构得到Teager能量谱,对重构信号进行时频分析并将其结果与原信号的希尔伯特黄变换(HilbertHuang transform,简称HHT)得到的边际谱进行对比。实验研究结果表明,本研究方法相比HHT能够对齿轮微弱故障特征进行更为有效地提取,验证了本研究方法在齿轮箱微弱故障诊断中的可行性。

Weak Fault Diagnosis Method of Gearbox Based on Improved Wavelet Denoising-Teager Energy Operator

In order to solve the problem that the characteristic of the weak fault vibration signal of gearbox was not easy to be extracted in the strong noise background, a weak fault diagnosis method based on improved wavelet denoising pretreatment and Teager-kaiser energy operator is presented. The original signal is denoised by the method of wavelet improved threshold function; the signal-to-noise ratio is improved effectively compared to morphological filter method and traditional threshold function method. The denoised signal is composed into several intrinsic mode functions(IMFs) by ensemble empirical mode decomposition (EEMD). The correlation coefficients of each IMF component and the original signal are calculated, and the effective components are screened by combining the spectrum of each IMF component. A time-frequency analysis result of reconstruction signal that used the effective IMF components to get the reconstructed Teager energy spectrum, is compared with a marginal spectrum that used HHT to original signal. The comparison research results show that the proposed method is more effective to extract the week characteristics of gear fault. And the results also prove the proposed method worked.