DT-CWT相关滤波在齿轮箱故障诊断中的应用
DT-CWT Domain Correlation Filter and Its Application in Incipient Gearbox Fault Diagnosis

根据小波系数的相关分析理论，提出了基于双树复小波变换的小波相关滤波法。该方法根据相邻层小波系数的相关性，通过迭代过程自适应地进行滤波，能够在达到良好降噪效果的同时保留微弱故障特征信息。对降噪后的信号进行希尔伯特包络分析便可准确得到故障特征频率。试验信号分析与工程应用结果表明，该方法能够有效提取强背景噪声下的齿轮箱轴承早期故障特征信息。
The conventional wavelet denoising method based on the simple threshold principle cannot always successfully extract the weak fault feature from the vibration signal with strong background noise, as the noise of different layers is individually estimated. Inspired by the fact that the wavelet transform coefficients of adjacent layers have some similarities where the signal is singular, a new method based on dual-tree complex wavelet transform and correlation filter is proposed. The method is an interactive process in which its parameters are adaptively selected and the noise can be efficiently reduced. More important, the faint component that is expected to be extracted will be retained. The defect frequency can be accurately found by the envelope demodulation analysis. As an improvement to the conventional wavelet transform domain correlation filter, it takes full advantage of dual-tree complex wavelet transform. Experimental and engineering application examples show the method's effectiveness in incipient gearbox fault diagnosis.