基于复合评价指标的金属微粒声发射信号最优小波去噪及其特征提取

Optimal Wavelet Denoising and Feature Extraction of Acoustic Emission Signal Caused by the Metal Particles by Using Composite Evaluation Index

金属微粒污染会使直流气体绝缘金属封闭输电线路(gas insulation line,GIL)的绝缘强度明显下降,微粒与电极碰撞产生的声发射信号可以反映其运动状态,但声发射信号的检测经常伴随着噪声污染。文中构建了一个适用于声发射信号的小波去噪复合评价指标,提出了一种基于此复合评价指标的最优小波去噪算法。对实验平台实测的声发射信号和加入有色噪声的仿真声发射信号的去噪表明,提出的复合评价指标相对于传统单一评价指标能较好地评价声发射信号的去噪效果;基于此复合指标的最优小波去噪算法能够对声发射信号有效去噪。基于去噪后的声发射信号,文中提取了信号的能量特征、微粒运动速度特征,拟合了二者与极间电场强度的相关性及表达式,结果表明,随着极间电场强度的增大,微粒运动速度和信号能量均以一次函数趋势增长。

Metal particles weaken the dielectric strength of DC GIL significantly. Acoustic emission signals caused by the impact between metal particles and grounding electrode can reflect the motion state of the metal particles. The acoustic emission signal often accompanied by noise in the process of the detection. This paper built a wavelet denoising composite evaluation index which is suitable for acoustic emission signals. By using that composite eval- uation index, this paper came up with an optimal wavelet de-noising algorithm. It is shown that through the denois- ing of the signals obtained from experiment platform and the signals artificially added colored noise, the composite evaluation indexes proposed in this paper can evaluate the noise effect of acoustic emission signals better than that of traditional single evaluation indexes. The optimal wavelet de-noising algorithm based on this composite index can effectively denoising acoustic emission signals. Furthermore, based the de-noised acoustic emission signals, this paper extracted the energy characteristics of the signal and the velocity of metal particles, fitted the correlation between electric field intensity and the characteristics of the signal mentioned before. The results indicate that,with the increase of electrode electric field strength, particle velocity and signal energy trend to increase as a linear function.