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Neural network fatigue life prediction in steel i-beams using mathematically modeled acoustic emission data.

机译：使用数学建模的声发射数据预测钢i型钢的神经网络疲劳寿命。

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摘要

The purpose of this research is to predict fatigue cracking in metal beams using mathematically modeled acoustic emission (AE) data. The AE data was collected from nine samples of steel Ibeam that were subjected to three-point bending caused by cyclic loading. The data gathered during these tests were filtered in order to remove long duration hits, multiple hit data, and obvious outliers. Based on the duration, energy, amplitude, and average frequency of the AE hits, the filtered data were classified into the various failure mechanisms of metals using NeuralWorksRTM Professional II/Plus software based self-organizing map (SOM) neural network. The parameters from mathematically modeled AE failure mechanism data were used to predict plastic deformation data. Amplitude data from classified plastic deformation data is mathematically modeled herein using bounded Johnson distributions and Weibull distribution. A backpropagation neural network (BPNN) is generated using MATLABRTM. This BPNN is able to predict the number of cycles that ultimately cause the steel I-beams to fail via five different models of plastic deformation data. These five models are data without any mathematical modeling and four which are mathematically modeled using three methods of bounded Johnson distribution (Slifker and Shapiro, Mage and Linearization) and Weibull distribution. Currently, the best method is the Linearization method that has prediction error not more than 17%. Multiple linear regression (MLR) analysis is also performed on the four sets of mathematically modeled plastic deformation data as named above using the bounded Johnson and Weibull shape parameters. The MLR gives the best prediction for the Linearized method which has a prediction error not more than 2%. The final conclusion made is that both BPNN and MLR are excellent tools for accurate fatigue life cycle prediction.
机译：这项研究的目的是使用数学建模的声发射（AE）数据预测金属梁的疲劳裂纹。 AE数据是从九个Ibeam钢样品中收集的，这些样品受到了循环载荷的三点弯曲作用。在这些测试期间收集的数据经过过滤，以去除长时间的匹配，多个匹配数据和明显的异常值。根据AE命中的持续时间，能量，幅度和平均频率，使用基于NeuralWorksRTM Professional II / Plus软件的自组织图（SOM）神经网络将过滤后的数据分类为金属的各种失效机理。数学建模的AE失效机制数据中的参数用于预测塑性变形数据。来自分类的塑性变形数据的振幅数据在本文中使用有界的约翰逊分布和威布尔分布进行数学建模。使用MATLABRTM生成反向传播神经网络（BPNN）。通过五个不同的塑性变形数据模型，该BPNN能够预测最终导致钢工字钢失效的循环次数。这五个模型是没有任何数学建模的数据，而四个模型是使用有界Johnson分布（Slifker和Shapiro，Mage和线性化）和Weibull分布的三种方法进行数学建模的。当前，最好的方法是线性化方法，其预测误差不超过17％。还使用有界的Johnson和Weibull形状参数对上述四组数学建模的塑性变形数据进行了多元线性回归（MLR）分析。 MLR为线性化方法提供了最佳预测，该方法的预测误差不超过2％。得出的最终结论是，BPNN和MLR都是准确预测疲劳寿命周期的出色工具。

著录项

  • 作者

    Selvadorai, Prathikshen N.;

  • 作者单位

    Embry-Riddle Aeronautical University.;

  • 授予单位 Embry-Riddle Aeronautical University.;
  • 学科 Engineering Aerospace.
  • 学位 M.S.A.E.
  • 年度 2012
  • 页码 237 p.
  • 总页数 237
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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