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Investigation of Damage Identification of 16Mn Steel Based on Artificial Neural Networks and Data Fusion Techniques in Tensile Test

机译:拉伸试验中基于人工神经网络和数据融合技术的16Mn钢损伤识别研究

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摘要

This paper proposes a damage identification method based on back propagation neural network (BPNN) and dempster-shafer (D-S) evidence theory to analyze the acoustic emission (AE) data of 16Mn steel in tensile test. Firstly, the AE feature parameters of each sensor in 16Mn steel tensile test are extracted. Secondly, BPNNs matching sensor number are trained and tested by the selected features of the AE data, and the initial damage decision is made by each BPNN. Lastly, the outputs of each BPNN are combined by D-S evidence theory to obtain the finally damage identification of 16Mn steel in tensile test. The experimental results show that the damage identification method based on BPNN and D-S evidence theory can improve damage identification accuracy in comparison with BPNN alone and decrease the effect of the environment noise.
机译:提出了一种基于反向传播神经网络(BPNN)和Dempster-shafer(D-S)证据理论的损伤识别方法,以分析16Mn钢在拉伸试验中的声发射(AE)数据。首先,提取了16Mn钢拉伸试验中每个传感器的声发射特征参数。其次,通过AE数据的选定特征对与传感器编号匹配的BPNN进行训练和测试,然后由每个BPNN做出初始损坏决策。最后,通过D-S证据理论将每个BPNN的输出进行组合,以得到16Mn钢在拉伸试验中的最终损伤识别。实验结果表明,与单独的BPNN相比,基于BPNN和D-S证据理论的损伤识别方法可以提高损伤识别的准确性,并减少环境噪声的影响。

著录项

  • 来源
  • 会议地点 Beijing(CN);Beijing(CN)
  • 作者单位

    Key Laboratory of Aerospace Materials and Performance (Ministry of Education),School of Materials Science and Engineering, Beihang University,100191 Beijing;

    Key Laboratory of Aerospace Materials and Performance (Ministry of Education),School of Materials Science and Engineering, Beihang University,100191 Beijing;

    Key Laboratory of Aerospace Materials and Performance (Ministry of Education),School of Materials Science and Engineering, Beihang University,100191 Beijing;

    Key Laboratory of Aerospace Materials and Performance (Ministry of Education),School of Materials Science and Engineering, Beihang University,100191 Beijing;

  • 会议组织
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 TP311.13;
  • 关键词

    acoustic emission; damage identification; back propagation neural network; dempster-shafer evidence theory;

    机译:声发射损坏识别;反向传播神经网络Dempster-Shafer证据理论;

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