柴油机振动信号的小波包奇异值降噪

De-noising of diesel vibration signal using wavelet packet and singular value decomposition

柴油机的振动信号中含有大量噪声,在进行故障特征提取之前必须加以消除。首先对傅里叶滤波降噪、小波降噪和小波包降噪的效果进行了对比,然后将奇异值分解技术用于信号降噪,最后提出了一种将小波包和奇异值分解相结合的降噪方法。该方法将输入信号进行一次小波包分解,利用奇异值分解方法对分解后的幅值量化系数进行降噪。实例表明,小波包和奇异值分解相结合的方法降噪效果最好。与其他方法相比,用新的方法对柴油机缸盖振动信号进行降噪处理的信噪比最高,且能明显识别出燃烧爆发、气门落座等各个阶段的振动信号,大大提高了特征提取的准确率。

The vibration signals of diesel include much noise that must be eliminated before characteristic parameters extraction. Firstly, the effects of vibration-signal de-noising among Fourier transform, wavelet decomposition and wavelet packet decomposition were compared. Secondly, the singular value decomposition was applied to de-noise vibration signals. Finally, a new de-noising method integrated with wavelet packet and singular value was put forward. In this method, vibration signals are decomposed by wavelet packet, and the wavelet packet coefficient is de-noised by singular value decomposition again. The results indicate that the new de-noising method is the best. The signal-to-noise ratio of the vibration signals of diesel cylinder lid is the highest. The diesel vibration wave-form of combustion and valve gets clearly and the extracted characteristic parameters become more precise.