黄金科学技术 ›› 2022, Vol. 30 ›› Issue (2): 209-221.doi: 10.11872/j.issn.1005-2518.2022.02.162
Xuebin XIE(),Tao LIU(
),Huan ZHANG
摘要:
声发射源的准确分类识别是声发射地压监测预报预警研究的重要基础。针对矿山井下围岩体声发射事件信号和采掘作业噪声信号分类识别问题,提出了一种基于改进完备总体经验模态分解和深度卷积神经网络(DCNN)的智能识别分类方法。首先,对信号进行改进CEEMDAN降噪处理,即利用相关性系数阈值和排列熵(PE)阈值剔除伪分量和噪声分量;然后,利用DCNN对降噪后的信号自动提取高维特征;最后,将特征用于softmax分类器分类识别,实现智能化井下信号源多分类。研究表明:改进CEEMDAN能够有效剔除伪分量及噪声分量;相比其他机器学习方法,改进CEEMDAN-DCNN方法具有准确率高和稳定性较好等优点。信号源识别分类方法研究为地压监测预警预报提供了重要的基础数据,准确的灾害预警预报可为矿山井下作业人员和设备提供安全保障。
中图分类号:
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