基于相关分析与小波变换的齿轮箱故障诊断

Fault Diagnosis Research Based on Correlation and Wavelet for Gears

针对大型机械工作噪声大,测取的振动信号信噪比很低,特征信号频率较高,信号消噪难度大,故障特征信号难以提取的问题,提出了一种基于相关性分析与小波变换相结合的故障诊断方法。该方法利用了相关函数降噪特性和小波多分辨特性,达到有效提取有用信号的目的。通过仿真与实验,证明这种方法能有效去除噪声,对故障特征信号有很强的提取能力。

When a large machine is working,the signal-to-noise ratio（SNR） of its vibration signal is very low and the frequency of characteristic signal is very high. The signal is overwhelmed by strong background noise, so it is difficult to pick it up. A new diagnosis method by using correlation analysis and wavelet transform was introduced. The correlation function has been used to suppress the noise and the wavelet for multi-resolution analysis. The effectiveness of suppressing noise and robust ability of detecting fault feature by using this method have been proved by both simulation and experiments done on a gun-automaton gearbox.