基于机器学习的煤岩破裂电磁辐射有效信号智能辨识研究
Intelligent Recognition of Electromagnetic Effective Signals Caused by Coal and Rock Fracture Based on Machine Learning Methods
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摘要: 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,易受井下环境干扰进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有效电磁信号是该技术应用推广的关键。为此,本文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方法建立了电磁辐射有效信号和干扰信号智能识别模型,并对比分析了不同模型识别精度。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方法对有效信号和干扰信号识别准确率均有影响,对比分析得到基于全部特征集的集成学习法识别准确率最高,对于两类信号的平均识别准确率为95.4%,能够满足电磁辐射监测预警应用需求。Abstract: Electromagnetic radiation (EMR), as an effective monitoring technology, has been applied to monitoring and early warning of coal-rock dynamic disasters such as rockburst, coal and gas outburst and so on. However, due to the complexity of the EMR signal generation mechanism, it is susceptible to interference from the underground environment and thus affects the accuracy of disaster risk monitoring and warning. Accurate identification of effective EMR signals induced by coal-rock rupture is the key to the application and promotion of this technology. For this purpose, EMR monitoring experiments under uniaxial compression conditions of coal-rock were carried out. The time-domain, frequency-domain and fractal characteristic variability of effective and interfering signals of EMR were analyzed. On this basis, machine learning methods such as linear discriminant method, support vector machine and integrated learning method were utilized to establish the intelligent identification models of effective and interfering signals of EMR, respectively. Moreover, the recognition accuracy of different models was compared and analyzed. The results show that the fractal box dimension, average frequency, count and peak frequency characteristics are more obvious to distinguish between effective and interfering signals of EMR, and the identification accuracy of single characteristic is above 70%. Both the signal characteristic set and the machine learning method have an impact on the recognition accuracy of both effective and interfering signals. Comparative analysis yields the highest recognition accuracy of the integrated learning method based on all feature sets, with an average recognition accuracy of 95.4% for the two types of signals. It can meet the needs of electromagnetic radiation monitoring and early warning applications.
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