Acoustic emission and electromagnetic radiation of coal-rock effective and interference signal identification utilizing generative adversarial learning and image feature mining

被引:1
|
作者
Zhao, Shenglei [1 ,2 ,3 ,4 ]
Wang, Enyuan [1 ,2 ,3 ,4 ]
Wang, Jinxin [1 ,2 ,3 ,4 ]
Wang, Dongming [1 ,2 ,3 ,4 ]
Li, Zhonghui [1 ,2 ,3 ,4 ]
Zhang, Qiming [1 ,2 ,3 ,4 ]
机构
[1] China Univ Min & Technol, Sch Safety Engn, Xuzhou 221116, Peoples R China
[2] China Univ Min & Technol, Key Lab Gas & Fire Control Coal Mines, Minist Educ, Xuzhou 221116, Peoples R China
[3] China Univ Min Technol, State Key Lab Coal Mine Disaster Prevent & Control, Xuzhou 221116, Peoples R China
[4] Natl Mine Safety Adm, Key Lab Theory & Technol Coal & Rock Dynam Disaste, Xuzhou 221116, Peoples R China
基金
中国国家自然科学基金;
关键词
NOISE;
D O I
10.1063/5.0237119
中图分类号
O3 [力学];
学科分类号
08 ; 0801 ;
摘要
Acoustic Emission (AE) and Electromagnetic Radiation (EMR) are playing an increasingly important role in the field of coal and rock dynamic disaster early warning due to their accurate response to the evolution process. However, blasting, drilling, and other coal mine technical activities are easily to produce interference signals, which seriously affect the credibility of early warning information. Moreover, unbalanced samples and complex characteristic characterization cannot achieve accurate identification. This paper presents a novel identification method for effective and interference signal of AE and EMR based on generative adversarial learning and image feature mining. First, Kalman filter is applied to AE and EMR monitoring signals to remove noise and retain key features. The Wasserstein Generative Adversarial Network, then, resolves the imbalance between the sample numbers of effective and various types of interference signals to ensure generalization of the identification. The effective and interference signal samples are further converted graphically by Symmetrized Dot Pattern, and intuitive different distribution characteristics are obtained. Finally, the EfficientNet model accurately identified typical effective and six interference signals collected downhole. The practical case of a coal mine in Liaoning Province shows that the proposed method is feasible and effective, and can provide a basis for reliable early warning of coal and rock dynamic disasters.
引用
收藏
页数:15
相关论文
共 6 条
  • [1] Acoustic Emission and Electromagnetic Radiation of the Coal-Rock Composite under Uniaxial Compression
    Zhang, Junwen
    Chen, Yulong
    Bu, Xiaohu
    Song, Zhixiang
    INDIAN GEOTECHNICAL JOURNAL, 2024,
  • [2] Electromagnetic radiation interference signal recognition in coal rock mining based on recurrent neural networks
    Di, Yangyang
    Wang, Enyuan
    GEOPHYSICS, 2021, 86 (04) : K1 - K10
  • [3] Identification and prediction method for acoustic emission and electromagnetic radiation signals of rock burst based on deep learning
    Yang, Hengze
    Wang, Enyuan
    Song, Yue
    Chen, Dong
    Wang, Xiaoran
    Wang, Dongming
    Li, Jingye
    PHYSICS OF FLUIDS, 2024, 36 (07)
  • [4] Automatic recognition of effective and interference signals based on machine learning: A case study of acoustic emission and electromagnetic radiation
    Li, Baolin
    Wang, Enyuan
    Li, Zhonghui
    Cao, Xiong
    Liu, Xiaofei
    Zhang, Meng
    INTERNATIONAL JOURNAL OF ROCK MECHANICS AND MINING SCIENCES, 2023, 170
  • [5] Identification method for microseismic, acoustic emission and electromagnetic radiation interference signals of rock burst based on deep neural networks
    Di, Yangyang
    Wang, Enyuan
    Huang, Tao
    INTERNATIONAL JOURNAL OF ROCK MECHANICS AND MINING SCIENCES, 2023, 170
  • [6] Acoustic emission and electromagnetic radiation precursor signal identification and early warning of coal and gas outburst based on diffusion-semi-supervised classification method
    Liu, Binglong
    Li, Zhonghui
    Zang, Zesheng
    Wang, Enyuan
    Zhang, Chaolin
    Yin, Shan
    PHYSICS OF FLUIDS, 2024, 36 (12)