提高双树复小波的齿轮箱复合故障特征提取

针对双树复小波变换分解层数需要先验确定和重构后各子带出现的频率混叠现象,提出了一种改进双树复小波变换的齿轮箱复合故障特征提取方法。首先,确定双树复小波变换的分解层数和有效的子带;对得到的各子带进行去频率混叠,确保消除频率混叠现象,使每个子带仅含有唯一的特征频率;然后,用所提方法和现有VMD(Variational Mode Decomposition)进行对比,验证了所提方法的可行性;最后将所提方法应用于齿轮箱复合故障振动信号中,成功提取出齿轮剥落和轴承外圈故障。所提方法为齿轮箱复合故障特征提取提供了一种新的思路。

Compound Fault Feature Extraction of Gearbox with Improved Dual-tree Complex Wavelet Transform

For Dual-tree complex wavelet transform decomposition of the number of layers need to be determined prior to and the sub-band after the occurrence of frequency aliasing,an improved dual tree complex wavelet transform is proposed to deal with gearbox composite fault feature extraction.Firstly,the decomposition layer number and the effective sub-band of the dual-tree complex wavelet transform are adaptively determined. The obtained sub-bands are subjected to frequency removal,the sub-band FFT is used to determine the main frequency and its notch filtering is performed to ensure that the frequency aliasing is eliminated,so that each sub-band only contains a unique characteristic frequency.Then using the proposed method and the methods such as Variational Mode Decomposition,the noise-containing simulation signals are decomposed and compared to verify the feasibility of the proposed method.The proposed method is applied to the vibration signal of the gearbox composite fault and successfully extracted the gear spalling and bearing outer ring fault.The proposed method provides a new idea for the extraction of complex fault features of gearboxes.