基于LCD的自适应小波脊线解调及齿轮故障诊断

针对最佳小波参数的设定和齿轮裂纹故障振动信号频率成分复杂、信噪比低等问题,将遗传优化算法、小波脊线解调与局部特征尺度分解(local characteristic-scale decomposition,简称LCD)相结合,提出了基于LCD的自适应小波脊线解调方法。首先,采用LCD方法将原始信号分解为若干个内禀尺度分量(intrinsic scale component,简称ISC),并通过选择蕴含特征信息的ISC来实现信号降噪;然后,以小波能量熵为目标函数,采用遗传算法优化小波参数,得到自适应小波;最后,通过自适应小波分析提取ISC的小波脊线,从而实现对原始信号的解调分析。通过齿轮裂纹故障诊断实例验证了该方法的有效性和优越性。

Adaptive Wavelet Ridge Demodulation Based on LCD Method and Its Application for Gear Fault Diagnosis

The wavelet ridge can effectively extract the modulation features of the transient signal. However, the demodulation effect is greatly influenced by wavelet parameters. Local characteristic-scale decomposition (LCD) is a new time-frequency analysis method. By combining LCD de-noising and a genetic algorithm with wavelet ridge demodulation, a gear incipient fault diagnosis method using the LCD de-noising approach and adaptive wavelet ridge demodulation is proposed to extract transient information from the original signal with a low signal to noise ratio. Furthermore, te proposed method is applied to gear crack fault diagnosis. Firstly, a multi-component AM-FM is adaptively decomposed into a series of intrinsic scale components (ISC). At the same time, the special intrinsic scale component that contains a bundant feature information is selected to realize denoising. Secondly, the genetic algorithm is used to optimize wavelet parameters by wavelet energy entropy, thus attaining an adaptive wavelet. Lastly, the adaptive wavelet ridge demodulation method is used to extract instantaneous amplitude and instantaneous frequency. Experimental data analysis results show that the proposed method can be applied to gear crack fault diagnosis.