基于小波-倒频谱的齿轮故障诊断方法及应用

利用振动信号采集到的齿轮故障信息，依据点蚀的故障机理和频谱特征，采用小波分解将信号分解在不同频带，有效抑制了背景噪声，从而得到故障特征频带，获得周期性突变的故障信息。选择故障所处频带重构信号，对故障进行诊断。结合倒频谱方法可以有效地识别故障特征频率。结果表明小波分析与倒频谱相结合是齿轮故障检测中一种有效的诊断方法。

Gear diagnosis and applying base on the method of wavelet-cepstrum

Based on the vibration signal to collect the gear failure information, the signal was decomposed in different frequency bands using the wavelet analysis according to the failure mechanism and spectrum signature of the pitting corrosion. It conduced to restrain the noises effectively and get the periodic break information. Selecting the bands where the failure located recomposed the signal and diagnosed the failure. Combining the cepstrum method could identify the characteristic frequency availably. The result proves that the wavelet-cepstrum method has great validity in gear failure diagnosis.