School of Environment and Safety Engineering, North University of China
School of Safety Engineering, China University of Mining and Technology

电磁辐射作为一种有效监测技术已应用于冲击地压、煤与瓦斯突出等煤岩动力灾害监测预警，但因电磁信号产生机制复杂，易受井下环境干扰（干扰信号）而影响灾害危险监测预警准确性。准确辨识煤岩破裂诱发的电磁辐射信号（有效信号）是该技术应用推广的关键。开展了煤岩单轴压缩电磁辐射监测实验，分析了电磁辐射有效信号和干扰信号时域、频域及分形特征差异性，分别利用线性判别法、支持向量机和集成学习法等机器学习算法建立了电磁辐射有效信号和干扰信号智能辨识模型，并对比分析了不同模型的识别精度。结果表明：分形盒维数、平均频率、计数和峰值频率特征对电磁辐射有效信号和干扰信号区分较明显，单一特征识别准确率均在70%以上；信号特征集和机器学习算法对有效信号和干扰信号识别准确率均有影响，基于全部特征集的集成学习法识别准确率最高，对2类信号的平均识别准确率为94.5%，能够满足电磁辐射监测预警应用需求。

Electromagnetic radiation (EMR) has proven to be an effective monitoring technology for coal rock dynamic disasters, including underground rock burst and coal and gas outbursts. However, the intricate generation mechanisms of electromagnetic signal, coupled with interference from underground environments, can compromise the accuracy of disaster monitoring and early warning systems. Accurately identifying EMR signals induced by coal rock fractures (effective signals) is essential for the widespread application of this technology. This study conducted monitoring experiments on electromagnetic radiation during uniaxial compression of coal rock, analyzing the time-domain, frequency-domain, and fractal characteristics of both valid and interference signals. Machine learning algorithms, such as linear discriminant analysis, support vector machines, and ensemble learning methods, were utilized to develop intelligent identification models for effective and interference signals. A comparative analysis of the recognition accuracy across different models was performed. The results demonstrated that characteristics like fractal box dimension, average frequency, count, and peak frequency effectively distinguished between valid and interference signals, with single-feature recognition accuracy surpassing 70%. Both the feature set and the choice of machine learning algorithm significantly influenced the identification accuracy of valid and interference signals. The ensemble learning method, leveraging the complete feature set, achieved the highest identification accuracy of 94.5% for both signal types, fulfilling the requirements for EMR monitoring and early warning applications.

国家自然科学基金项目（5230042436）；国家资助博士后研究人员计划项目（GZC20241583）；山西省基础研究计划项目（202203021222031，202203021222043）。

李保林，冯嘉琪，王恩元，等. 基于机器学习的煤岩破裂诱发电磁辐射信号智能辨识研究[J]. 工矿自动化，2024，50（9）：144-152.

LI Baolin, FENG Jiaqi, WANG Enyuan, et al. Intelligent identification of electromagnetic radiation signals induced by coal rock fractures using machine learning[J]. Journal of Mine Automation，2024，50（9）：144-152.