中文摘要：

齿轮故障振动信号往往表现为非线性非平稳特性,并且早期故障振动信号往往包含较强的背景噪声,不利于故障特征的提取。针对该问题,提出了基于双树复小波变换和局部投影算法的齿轮故障诊断方法。首先,对故障信号进行双树复小波变换,得到不同尺度下的小波系数和最后一层的尺度系数,并计算各层小波系数的模与相角。然后,选择模周期性较强的小波系数或尺度系数进行局部投影算法处理,得到周期性增强的系数的模,并选择合适的阈值进行软阈值处理。最后,利用处理后的系数进行双树复小波重构,从而提取出齿轮故障特征信号,进行希尔伯特包络解调分析便能准确地得到故障特征频率。仿真信号和工程应用表明,该方法能够有效地提取齿轮故障特征信息,提供了一种齿轮故障特征提取的新方法。

英文摘要：

As gear fault vibration signal is always nonlinear and nonstationary and always with a strong background noise which result in difficulty of fault feature extraction,a new method based on dual-tree complex wavelet transform and local projective method is proposed.As a improved method of the conventional discrete wavelet transform（DWT）,dual-tree complex wavelet transform has many advantages over DWT,such as the improvement of frequency aliasing and oscillations of wavelet coefficients which is the key to the method proposed.Local projective method for nonlinear time series has a good ability of signal period strengthen and noise suppression,which fits for wavelet coefficients denoising.Firstly,the fault signal is decomposed by dual-tree complex wavelet transform to obtain the coefficients of different layers.Secondly,the nonlinear time series method is used to strengthen the periodicity of the coefficient whose amplitude is more periodic,and then do soft-threshold denoising.Finally,the fault characteristic signal can be obtained by coefficient reconstruction.The fault frequency can be located accurately by Hilbert envelope spectrum analysis.The simulation and engineering application showed the effectiveness of the method in early gear fault diagnose.