针对岩石在破裂过程中产生的大量含噪声发射信号问题,基于天鹰优化(AquilaOptimizer,AO)算法提出一种改进的变分模态分解(VMD)联合小波包分解的声发射(AE)信号去噪算法。利用Circle混沌优化的AO算法对VMD算法中的分解模态个数K和二次惩罚因子进行寻优,有效避免了人为经验选取参数导致的时间消耗以及效果不佳等问题;利用得到的最优参数组合对岩石破裂声发射信号进行分解,得到一系列本征模态分量,结合相关系数筛选出含噪分量;然后利用小波包去噪算法对含噪分量进行处理;最后,将未处理的分量与降噪处理后的分量重构得到去噪后的AE信号。通过仿真和实测信号分析,结果表明与现有去噪算法相比,该算法能更好地去除AE信号中的噪声分量,保留信号的频域特征信息,可为后续利用声发射信号探究岩体破裂规律提供参考。

A large number of noisy acoustic emission signals will be produced during the process of rock fracturing. An improved variational mode decomposition (VMD) combined with wavelet packet was proposed based on aquila optimizer (AO) algorithm for denoising of acoustic emission (AE) signal. The AO algorithm based on Circle chaosoptimization was used to optimize the decomposition modes K and the quadratic penalty factor α in VMD,avoiding the issues such as time consumption and poor performance caused by the manual selection of parameters based on experience. The obtained optimal parameter combination was employed to decompose rock fracture AE signals,and a series of intrinsic modal components were obtained. The noisy components were filtered out via combining intrinsic modal components with correlation coefficients. The wavelet packet denoising algorithm was used to process the noisy components. The unprocessed components and denoised components were reconstructed to obtain the denoised AE signal. Through the simulation and testing results indicated that the proposed algorithm could remove the noise components from AE signals better than the existing denoising algorithms,and efficiently preserved the frequency domain characteristic information of the signal. The findings in this study would provide a reference for further exploring the rules of rock fracture using AE signals.