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一种基于多尺度融合的深度学习方法,用于对抗噪音的煤电带识别.

Qingjun Song1, Shirong Sun1, Qinghui Song1

  • 1College of Intelligent Equipment, Shandong University of Science and Technology, Taian, 271000, Shandong, China.

Scientific reports
|January 2, 2025
PubMed
概括

这项研究引入了一种新的多尺度卷积神经网络 (MCNN-BILSTM),用于在杂的采矿环境中准确识别煤炭帮派. 该方法提高了工业应用的稳定性和适应性.

关键词:
煤带识别技术 煤带识别技术多个尺度的并行神经网络.注意力机制注意力机制振动信号是一个振动信号.

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科学领域:

  • 采矿工程 采矿工程 采矿工程
  • 人工智能的人工智能
  • 信号处理 信号处理

背景情况:

  • 对于智能采矿和煤炭质量来说,对煤炭链的识别至关重要.
  • 现有的方法与尘埃和噪音作斗争,限制了工业用途.
  • 在恶劣环境中准确识别仍然是一个挑战.

研究的目的:

  • 为杂的工业环境开发一个强大的煤炭连锁识别方法.
  • 为了提高煤识别系统的准确性和稳定性.
  • 增强集成工作面的智能实现.

主要方法:

  • 提出了一个端到端的多级特征融合卷积神经网络 (MCNN-BILSTM).
  • 使用多尺度学习和注意力机制分析振动信号.
  • 传统的过方法与深度学习相结合.

主要成果:

  • 该MCNN-BILSTM方法表现出强大的适应性和稳定性.
  • 该方法在复杂的环境中显示出显著的抗噪能力.
  • 实验验证是在一个煤冲击液压支平台上进行的.

结论:

  • 拟议的MCNN-BILSTM方法适用于复杂的实际工业场所.
  • 该技术有效地克服了现有的煤识别系统的局限性.
  • 这一进步有助于实现更安全,更有效的煤炭开采操作.