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首页> 外文期刊>NDT & E international >Characterisation of clustered cracks using an ACFM sensor and application of an artificial neural network
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Characterisation of clustered cracks using an ACFM sensor and application of an artificial neural network

机译：使用ACFM传感器表征团簇裂纹及其在人工神经网络中的应用

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摘要

The alternating current field measurement (ACFM) technique can be applied for surface-breaking fatigue crack detection and sizing; the link between the ACFM signal and crack size is well understood for individual cracks. However, the ACFM response to multiple clustered cracks is significantly different to that of isolated cracks. In railway rails the high wheel-rail forces can lead to rolling contact fatigue (RCF) cracks. Often cracks appear together in small clusters or in long stretches. The accurate characterisation of such fatigue cracks is essential for carrying out efficient and safe repair and maintenance. This paper presents a method for sizing the important sub-surface section of multiple cracks using ACFM via the application of an artificial neural network (ANN). The approach is demonstrated using a railway case study: a simulation-based dataset of signal response covering the range of RCF cracks typically seen in in-service railway tracks has been generated to give a thorough representation of the effect of clustered crack parameters on the ACFM response. A 5 × 5 × 2 × 1 multi-layer ANN has been optimised and trained using the validated simulation database to learn the inverse relationship between the crack pocket length (desired output) and the ACFM signal for a given cluster of RCF cracks. The network has been evaluated on a set of experimental data to size cracks of known dimensions from ACFM measurements and also on unseen simulation data. Results from both simulation and experiment show that the approach presented can be used to size clustered cracks to approximately the same degree of accuracy as is possible for isolated cracks.
机译：交流场测量（ACFM）技术可用于表面断裂疲劳裂纹的检测和定型；对于单个裂纹，ACFM信号和裂纹尺寸之间的联系已广为人知。但是，ACFM对多个聚集裂纹的响应与孤立裂纹的响应明显不同。在铁路轨中，较高的轮轨力会导致滚动接触疲劳（RCF）裂纹。裂纹经常一起出现在小的簇中或长时间延伸。这种疲劳裂纹的准确表征对于进行有效，安全的维修和保养至关重要。本文提出了一种通过使用人工神经网络（ANN）应用ACFM来确定多个裂纹的重要子表面区域的方法。通过铁路案例研究证明了该方法：已经生成了基于仿真的信号响应数据集，涵盖了在役铁路轨道中常见的RCF裂纹范围，以全面表示裂纹簇参数对ACFM的影响响应。已经使用经过验证的仿真数据库对5×5×2×1多层ANN进行了优化和训练，以了解给定RCF裂纹簇的裂纹袋长度（所需输出）与ACFM信号之间的反比关系。已根据一组实验数据对网络进行了评估，以根据ACFM测量结果对已知尺寸的裂纹进行尺寸调整，并对未知的模拟数据进行了评估。仿真和实验结果均表明，所提出的方法可用于对簇状裂纹进行尺寸调整，使其精度与孤立裂纹几乎相同。

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  • 来源
    《NDT & E international》 |2018年第9期|80-88|共9页
  • 作者

    H. Rowshandel; G.L. Nicholson; J.L. Shen; C.L. Davis;

  • 作者单位

    Birmingham Centre for Railway Research and Education University of Birmingham;

    Advanced Steel Research Centre WMG University of Warwick;

    Birmingham Centre for Railway Research and Education University of Birmingham|Advanced Steel Research Centre WMG University of Warwick;

  • 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
  • 关键词

    ACFM; Automated fault diagnosis; Clustered cracks; ANN;

    机译：ACFM;自动化故障诊断;裂纹聚集;人工神经网络;

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