自适应冗余第2代小波设计及齿轮箱故障特征提取

针对强噪声背景下齿轮箱故障特征的提取问题,设计了一种提取该类信号时域特征的自适应冗余第2代小波.采用基于数据的优化算法设计每层小波分解的初始预测器和更新器,然后通过对初始预测器和更新器进行插值补零运算,来获得冗余预测器和更新器.第2代小波不需要剖分运算,利用冗余预测器和更新器直接对每层逼近信号进行预测和更新运算,能较好地保留信号的时域特征.采用第2代小波较理想地提取出了齿轮箱发生摩擦故障时的时域调制波形和周期性冲击脉冲,并对得到的细节和逼近信号进一步进行包络解调,从而分离出了故障调制源频率.结果表明,自适应冗余第2代小波对噪声背景下齿轮箱故障特征的提取效果优于其他小波.

Adaptive Redundant Second Generation Wavelet Design and Gearbox Fault Feature Extraction

To extract fault feature of gearbox signal corrupted by noise, a novel method to design adaptive redundant second generation wavelet (ARSGW) is developed, where the data-based optimization algorithm is adopted to design the initial prediction operator and update operator at each decomposition level, the initial prediction operator and update operator are interpolated with zero, and the redundant prediction operator and redundant update operator are gained. Splitting operation is unnecessary for ARSGW, and the approximation signal at each level is predicted and updated directly to retain the signal characteristics in time domain. The demodulation waveform and periodic impulses from the rub fault vibration signal of a large air compressor gearbox are desirably extracted with ARSGW, the approximation signal is demodulated, and the rotating frequency of high speed axis is regarded as the modulation frequency. Engineering results confirm that ARSGW outperforms the other wavelet.