基于支持向量机的煤自燃声学预警方法研究  

Research on acoustic early warning method for coal spontaneous combustion based on optimization support vector machine

作　　者：孔彪[1] 郑永超 冯新 刘纪凡 Kong Biao;Zheng Yongchao;Feng Xin;Liu Jifan(Department of Safety Engineering,School of Safety and Environmental Engineering,Shandong University of Science and Technology,Qingdao 266590,China)

机构地区：[1]山东科技大学安全与环境工程学院,安全工程系,青岛266590

出　　处：《清华大学学报(自然科学版)》2025年第4期769-776,共8页Journal of Tsinghua University(Science and Technology)

基　　金：国家自然科学基金面上项目(52374219);山东省自然科学基金面上项目(ZR^(2)023ME115);山东省高等学校青创科技支持计划(2023KJ086)。

摘　　要：煤自燃火灾监测预警是制约煤炭行业安全发展的一大难题,现有煤自燃监测预警方法中,采用单一指标分析精确度不足,基于统计分析法的多指标判定方法会受指标数量和种类的限制,使得判定过程复杂,结果差异较大,而支持向量机算法可以从有限的样本中学习出全部的规律,有望应用于煤自燃监测预警领域。该文首先建立煤自燃过程中次声波和声发射信号测试系统,研究发现次声波和声发射的主频幅值与温度具有较高的相关性(相关系数R^(2)>0.90),可作为监测煤自燃的有效指标。其次,在分析支持向量机原理的基础上,将次声波和声发射的主频幅值作为特征向量,选用“一对一”方法建立煤自燃支持向量机模型,并验证分析不同核函数对识别结果的影响,形成基于支持向量机的煤自燃声学预警方法。研究结果表明,该支持向量机模型能根据煤自燃产生的次声波、声发射主频幅值数据特征较好地划分煤自燃的不同阶段,其中次声波多项式核函数支持向量机与声发射Gauss核函数支持向量机的分类效果最好,总体识别率在90.00%以上。本文的研究为煤自燃高效监测预警提供了一种新方法。[Objective]Monitoring and providing early warnings of spontaneous combustion fires in coal pose a significant challenge,hindering the safe advancement of the coal industry.Currently,the accuracy of single-indicator analysis in existing monitoring and early warning methods is insufficient.Multi-indicator judgment methods that rely on statistical analysis are limited by the number and types of indicators,making the judgment process complex and resulting in significant differences.Support vector machine algorithms that are capable of learning rules from limited samples,show potential for application in coal spontaneous combustion monitoring and early warning.[Methods]This study establishes a testing system for infrasound waves and acoustic emission signals during coal spontaneous combustion.It explores the relationship between the main frequency amplitude of these signals and temperature to determine whether they can serve as feature vectors for support vector machines.Based on this relationship,the coal spontaneous combustion process is divided into three stages:early,middle,and late,and a combustion support vector machine model is established.The models are trained with different kernel functions,and the one with the highest recognition accuracy for the three periods early,middle,and late stages of coal spontaneous combustion for further validation.Finally,untreated experimental data is used to validate the model's recognition performance.[Results](1)There is a positive correlation between the amplitude of infrasound waves and the main frequency of acoustic emission with temperature,and the correlation coefficient R^(2) is high,all of which are above 0.90.This shows their effectiveness as indicators for monitoring coal spontaneous combustion.(2)The subsonic polynomial kernel support vector machine can accurately identify the three periods before,during,and after coal spontaneous combustion,outperforming linear and Gaussian kernel support vector machines.Meanwhile,the acoustic emission Gaussian kernel support vecto

关 键 词：煤自燃 支持向量机 次声波 声发射 预测预警 

分 类 号：X936[环境科学与工程—安全科学]