基于特征增强倒频谱分析的齿轮故障诊断方法

Gear Fault Diagnosis Method based on Feature-enhanced Cepstrum Analysis

齿轮发生故障后,由于采集到的振动信号同时包含故障冲击、确定性啮合信号及噪声等多种信号,同时,各种信号还会受传递路径的影响,使得齿轮故障特征提取难度较大。倒频谱分析是常见的齿轮故障诊断方法,能将边频带中的周期成分显示为单根谱线,有助于故障诊断,但当故障特征信号较微弱时,倒频谱中得到的故障特征并不明显。为此,提出一种特征增强倒频谱分析方法,利用最小熵解卷积、自回归线性预测和小波去噪3种特征增强方法,逐步增强齿轮振动信号中的故障冲击特征,再利用倒频谱进行故障特征提取。通过实验,验证了所提方法的有效性。

After the gear fault occurs,the vibration signal collected includes fault impact,deterministic meshing signal and noise signal,and these signals are also affected by the transmission path,which makes the gear fault feature extraction difficult.Cepstrum analysis is a common method for gear fault diagnosis.It can dis play the periodic components in the sideband as a single line,which is helpful for fault diagnosis.However,when the fault characteristic signal is weak,the fault characteristics in the cepstrum are not obvious.For this,feature-enhanced cepstrum analysis method is proposed.Three feature enhancement methods minimum entropy deconvolution,autoregressive linear prediction and wavelet de-noising are used to enhance the fault impact characteristics of gear vibration signals,and then the cepstrum is used to extract the fault features.The effective ness of the proposed method is verified by experiment.