| 李鑫,刘志勇,杨桢,李昊,周婧,卜婧然,王艺儒.基于CEEMD-IDWT的受载煤岩微震电压去噪算法[J].电子测量与仪器学报,2024,38(8):124-136 |
| 基于CEEMD-IDWT的受载煤岩微震电压去噪算法 |
| Microseismic voltage denoising algorithm for loadedcoal rock based on CEEMD-IDWT |
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| DOI： |
| 中文关键词: 受载煤岩 微震电压 互补集合经验模态分解 改进dmey小波 去噪算法 |
| 英文关键词:loaded coal rock microseismic voltage complementary ensemble empirical mode decomposition improved discrete meyer wavelet transform denoising algorithm |
| 基金项目:国家自然科学基金（51604141，51204087）、辽宁省应用基础研究计划项目（2023JH2/101300138）、辽宁省教育厅基本科研项目（重点攻关项目）(LJKZZ20220046，JYTZD2023075)资助 |
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| Author | Institution |
| Li Xin | School of Electrical and Control Engineering, Liaoning Technical University, Huludao 125105, China |
| Liu Zhiyong | School of Electrical and Control Engineering, Liaoning Technical University, Huludao 125105, China |
| Yang Zhen | School of Electrical and Control Engineering, Liaoning Technical University, Huludao 125105, China |
| Li Hao | School of Electrical and Control Engineering, Liaoning Technical University, Huludao 125105, China |
| Zhou Jing | School of Electrical and Control Engineering, Liaoning Technical University, Huludao 125105, China |
| Bu Jingran | School of Electrical and Control Engineering, Liaoning Technical University, Huludao 125105, China |
| Wang Yiru | School of Electrical and Control Engineering, Liaoning Technical University, Huludao 125105, China |
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| 中文摘要: |
| 受载复合煤岩变形破裂过程中产生的微小震动信号包含煤岩内部结构破裂信息，传统设备采集的微震信号存在大量环境噪声而无法直接进行分析。为有效提取受载煤岩变形破裂过程微震信号的变化特征，采用互补集合经验模态分解算法(CEEMD)与改进dmey小波(IDWT)算法相融合，提出一种新型CEEMD-IDWT联合去噪算法。该算法首先利用CEEMD算法对原始信号进行分解，然后对分解得到的IMF分量应用IDWT算法进行去噪处理，最终将处理过的分量进行重构得到去噪信号。利用仿真分析和单轴压缩实验对该算法的有效性进行验证，结果表明：CEEMD-IDWT联合算法在仿真分析中，相比传统算法信噪比最大提高204.5%，对于其他改进去噪算法信噪比最少提高11.8%，去噪能力具有明显优势；将该算法嵌入自研微震电压采集设备，在复合煤岩单轴压缩实验中得到的微震电压信号噪噪比仅为0.089 75，实际去噪效果明显；经CEEMD-IDWT联合算法去噪之后的微震电压具有明显的变化特征，显著提升了信号去噪效果，有效避免了微震电压信号的失真，可以作为受载煤岩变形破裂微震电压信号去噪处理的理想算法，为煤岩动力灾害的准确预判提供了一种可靠且先进的技术参考。 |
| 英文摘要: |
| The microseismic signals generated during the deformation and rupture of loaded composite coal rock contain information about the rupture of the internal structure of the coal rock, and the microseismic signals collected by traditional equipment cannot be analyzed directly because of the presence of a large amount of environmental noise. In order to effectively extract the change characteristics of the microseismic signals during the deformation and rupture of loaded coal rock, a new CEEMD-IDWT joint denoising algorithm is proposed by integrating the complementary ensemble empirical modal decomposition algorithm (CEEMD) with the improved dmey wavelet (IDWT) algorithm. The algorithm firstly utilizes the CEEMD algorithm to decompose the original signal, then applies the IDWT algorithm to denoise the IMF components obtained from the decomposition, and finally reconstructs the processed components to obtain the denoised signal. The effectiveness of the algorithm is verified using simulation analysis and uniaxial compression experiments, and the results show that: the CEEMD-IDWT joint algorithm improves the signal-to-noise ratio by a maximum of 204.5% compared with the traditional algorithm in simulation analysis, and increases the signal-to-noise ratio of other improved denoising algorithms by a minimum of 11.8%, which is an obvious advantage in denoising ability; the microseismic voltage obtained by embedding the algorithm into the self-researched microseismic voltage acquisition equipment is significantly higher than that obtained by the conventional algorithm in the uniaxial compression experiments on the composite coal rock. The noise-to-noise ratio of the microseismic voltage signal obtained in the compression experiment is only 0.089 75, and the actual denoising effect is obvious; the microseismic voltage after denoising by the joint CEEMD-IDWT algorithm has obvious change characteristics, which significantly improves the signal denoising effect and effectively avoids the distortion of the microseismic voltage signal, and can be used as an ideal algorithm for the denoising of deformation and rupture of the microseismic voltage signal of the loaded coal rock and provides an ideal algorithm to accurately predict the coal rock dynamics and disaster. It can be used as an ideal algorithm for de-noising the microseismic voltage signal of loaded coal rock deformation and rupture, which provides a reliable and advanced technical reference for the accurate prediction of coal-rock power disasters. |
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