岩石破坏全过程声发射信号去噪及特征提取方法研究

Study on method of acoustic emission signal denoising and feature extraction in whole process of rock failure

岩石在被破坏时存在的随机性和复杂性导致了采集信号的不确定性,降低了声发射信号特征提取的准确性,为了能有效地对岩石破裂过程进行监测,以红砂岩破裂过程为研究对象,创新地提出了一种基于完整集成经验分解算法-小波阈值和凸优化理论特征提取方法(CEEMDAN)。首先,用CEEMDAN法对信号进行分解,依据相关系数和方差贡献率选取真实分量,利用连续均方误差准则确定能量分界点。其次,进行小波阈值去噪,并与直接小波阈值、舍弃IMF1、舍弃IMF1和IMF2去噪等方法进行比较,利用信噪比和定位精度作为评价指标。最后对重构信号进行凸优化特征提取,通过试验验证了提出方法的可行性。试验结果表明:结合CEEMDAN法和小波阈值的去噪方法能有效地抑制高频噪声,而且去噪后信噪比和定位精度最高;随着轴向应力的增加,砂岩单轴压缩破裂过程表现为4个阶段,声发射事件数与应力-时间曲线规律相一致,且在失稳破坏阶段平均事件数所占比例最高为58.44%,研究结果可为定量监测岩体破裂过程及失稳现象提供了新的依据和方法。

The randomness and complexity resulting from the process of rock failure could result in uncertainty in acquisition signals hence lower accuracy of acoustic emission(AE)signal feature extraction under external load.In order to effectively monitor the process of rock fracture,red sand rock test was studied for its fracture process and an innovative signal processing method based on combining integrating empirical mode decomposition(CEEMDAN)and wavelet threshold and convex optimization is proposed for feature extraction.This method can retain and extract features of effective signals.Firstly,the AE signals under different rupture states collected from the experiments were decomposed by CEEMDAN,Then the sensitive intrinsic mode function which can reflect characteristic of signal was selected using correlation coefficient method and deviation contribution rate,moreover,the high frequency energy demarcation point was determined according to the continuous mean square error criterion.Secondly,wavelet threshold was performed to eliminate noise and the result was compared to result from other noise eliminating methods such as direct wavelet threshold,discarding IMF1,discarding IMF1 and IMF2.Signal noise ratio and positioning precision were used for assessing those methods.Finally,the reconstructed signal was convexly optimized and the feasibility of proposed method was verified by experiments.Study results confirm that the proposed combining CEEMDAN and wavelet threshold method can effectively curb high frequency noise and render highest signal noise ratio and positioning precision.It is also revealed that as the increase of axial stress the sandstone uniaxial compression cracking process exhibited four stages.The number of AE signal events is consistent with stress-time curves,and ratios of average number of events at unstable failure stage could be up to 58.44%.This study can provide new basis and method for quantitatively monitor cracking process and unstability of rock.