基于EWT-KLD的机械密封金刚石涂层磨损声发射降噪

Denoising of Acoustic Emission of Diamond-Coated Mechanical Seals Wear Based on Empirical Wavelet Transform and Kullback-Leibler Divergence

为了准确获得机械密封金刚石涂层在磨损过程的声发射信号,在分析机械密封设备的噪声特性基础上,提出了基于经验小波变换(EWT)和相对熵(KLD)的声发射降噪方法;通过对磨损声发射信号进行经验小波变换得到划分其频带的滤波器组,对磨损声发射信号和背景噪声发射信号用相同的滤波器组划分频带;计算相应频带2种信号的相对熵,用累计和算法在升序排列的相对熵中找到首个大于3σ的值作为阈值,保留相对熵值大于阈值的频带重构信号,完成降噪.研究结果表明:本文所提的EWT-KLD方法可以有效抑制不同工况、不同磨损状态的声发射信号的噪声,有效改善了磨损声发射信号的信噪比,尤其是微弱磨损信号的信噪比,提高了密封端面磨损声发射检测的精度和灵敏度;通过与传统降噪方法的对比发现,本文方法能够对不同工况下的密封磨损声发射信号降噪表现出更强的适应性和稳定性,对于及时检测早期密封磨损和准确监测磨损累积变化过程具有重要意义.

In order to obtain the pure wear acoustic emission of diamond-coated mechanical seal,the denoising method based on empirical wavelet transform(EWT)and Kullback-Leibler divergence(KLD)was proposed.Firstly,filter bank was calculated with empirical wavelet transform on acquired acoustic emission signal.Then the filter bank was applied to both the acquired acoustic emission signal and background noise acoustic emission signal.The Kullback-Leibler divergences were calculated between the corresponding bands of two signals.The cumulative sum algorithm was employed to find a threshold for determining whether the corresponding band is used for signal reconstruction.The results show that the proposed method can effectively suppress the noise of acoustic emission signals under different working conditions and wear states,and effectively improve the signalto-noise ratio of wear acoustic emission signals,especially weak wear signals.Compared with the traditional denoising methods,the proposed EWT-KLD method has stronger adaptability and stability for denoising of wear acoustic emission signal under different working conditions,which is of great significance for the monitoring early seal wear and the cumulative wear process of seal.