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首页> 外文期刊>Shock and vibration >Coal-Rock Recognition in Top Coal Caving Using Bimodal Deep Learning and Hilbert-Huang Transform
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Coal-Rock Recognition in Top Coal Caving Using Bimodal Deep Learning and Hilbert-Huang Transform

机译：基于双峰深度学习和希尔伯特-黄变换的放顶煤煤岩识别

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摘要

This study employs the mechanical vibration and acoustic waves of a hydraulic support tail beam for an accurate and fast coal-rock recognition. The study proposes a diagnosis method based on bimodal deep learning and Hilbert-Huang transform. The bimodal deep neural networks (DNN) adopt bimodal learning and transfer learning. The bimodal learning method attempts to learn joint representation by considering acceleration and sound pressure modalities, which both contribute to coal-rock recognition. The transfer learning method solves the problem regarding DNN, in which a large number of labeled training samples are necessary to optimize the parameters while the labeled training sample is limited. A suitable installation location for sensors is determined in recognizing coal-rock. The extraction features of acceleration and sound pressure signals are combined and effective combination features are selected. Bimodal DNN consists of two deep belief networks (DBN), each DBN model is trained with related samples, and the parameters of the pretrained DBNs are transferred to the final recognition model. Then the parameters of the proposed model are continuously optimized by pretraining and fine-tuning. Finally, the comparison of experimental results demonstrates the superiority of the proposed method in terms of recognition accuracy.
机译：这项研究利用了液压支架尾梁的机械振动和声波来进行准确，快速的煤岩识别。研究提出了一种基于双峰深度学习和希尔伯特-黄变换的诊断方法。双峰深度神经网络（DNN）采用双峰学习和转移学习。双峰学习方法试图通过考虑加速度和声压模态来学习联合表示，这两者都有助于煤岩识别。转移学习方法解决了与DNN有关的问题，在DNN中，需要大量标记训练样本来优化参数，而标记训练样本却受到限制。在识别煤岩时，确定传感器的合适安装位置。组合加速度和声压信号的提取特征，并选择有效的组合特征。双峰DNN由两个深度置信网络（DBN）组成，每个DBN模型都使用相关样本进行训练，并且将预训练DBN的参数转移到最终识别模型中。然后通过预训练和微调对所提出模型的参数进行连续优化。最后，实验结果的比较证明了该方法在识别精度方面的优越性。

著录项

  • 来源
    《Shock and vibration》 |2017年第4期|1-13|共13页
  • 作者

    Zhang Guoxin; Wang Zengcai; Zhao Lei; Qi Yazhou; Wang Jinshan;

  • 作者单位

    Shandong Univ, Sch Mech Engn, 17923 Jingshi Rd, Jinan 250061, Shandong, Peoples R China;

    Shandong Univ, Sch Mech Engn, 17923 Jingshi Rd, Jinan 250061, Shandong, Peoples R China;

    Shandong Univ, Sch Mech Engn, 17923 Jingshi Rd, Jinan 250061, Shandong, Peoples R China;

    Shandong Univ, Sch Mech Engn, 17923 Jingshi Rd, Jinan 250061, Shandong, Peoples R China;

    Shandong Univ, Sch Mech Engn, 17923 Jingshi Rd, Jinan 250061, Shandong, Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
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