Acoustic emission (AE) source localization is a powerful detection method. Time Difference Mapping (TDM) method is an effective method for detecting defects in complex structures. The core of this method is to search for a point with the minimum distance away from the verification point in the time difference database. In Traditional Time Difference Mapping (T-TDM) method and Improved Time Difference Mapping (I-TDM) method, the larger database and denser grids allow the higher localization accuracy. If the location points are not included in the database, the localization accuracy of the T-TDM and I-TDM methods will be greatly affected. To solve the above problems, a new AE source localization method, Generalized Regression Neural Network Based on Time Difference Mapping (GRNN-TDM), is proposed to improve the localization accuracy in the study. In the proposed method, the time difference data of the sensor path on all nodes in the time difference mapping are used as the training input data and the coordinates of grid nodes are used as the training output data. After continuous learning and training, the neural network model predicts its possible source location with the time difference data collected from the verification point. In this paper, the localization of AE sources with T-TDM, I-TDM and GRNN-TDM methods was studied in four composite plates with different fiber layers and an aluminum plate with holes. The localization results showed that the localization accuracy of the GRNN-TDM method was higher than that of T-TDM and I-TDM methods.

中文翻译：

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基于时差映射法的广义回归神经网络声发射源定位

声发射 (AE) 源定位是一种强大的检测方法。时差映射（TDM）方法是检测复杂结构缺陷的有效方法。该方法的核心是在时差数据库中寻找与验证点距离最小的点。在传统时差映射（T-TDM）方法和改进时差映射（I-TDM）方法中，更大的数据库和更密集的网格允许更高的定位精度。如果定位点不包含在数据库中，T-TDM 和 I-TDM 方法的定位精度将受到很大影响。针对上述问题，研究中提出了一种新的AE源定位方法——基于时差映射的广义回归神经网络（GRNN-TDM），以提高定位精度。该方法以时差映射中所有节点上传感器路径的时差数据作为训练输入数据，以网格节点坐标作为训练输出数据。神经网络模型经过不断的学习和训练，利用从验证点采集到的时间差数据预测其可能的源位置。本文在四种不同纤维层的复合板和一块带孔的铝板中研究了用 T-TDM、I-TDM 和 GRNN-TDM 方法定位声发射源。定位结果表明，GRNN-TDM方法的定位精度高于T-TDM和I-TDM方法。时差映射中所有节点上传感器路径的时差数据作为训练输入数据，网格节点的坐标作为训练输出数据。神经网络模型经过不断的学习和训练，利用从验证点采集的时差数据预测其可能的源位置。本文在四种不同纤维层的复合板和一块带孔的铝板中研究了用 T-TDM、I-TDM 和 GRNN-TDM 方法定位声发射源。定位结果表明，GRNN-TDM方法的定位精度高于T-TDM和I-TDM方法。时差映射中所有节点上传感器路径的时差数据作为训练输入数据，网格节点的坐标作为训练输出数据。神经网络模型经过不断的学习和训练，利用从验证点采集的时差数据预测其可能的源位置。本文在四种不同纤维层的复合板和一块带孔的铝板中研究了用 T-TDM、I-TDM 和 GRNN-TDM 方法定位声发射源。定位结果表明，GRNN-TDM方法的定位精度高于T-TDM和I-TDM方法。神经网络模型利用从验证点收集的时间差数据预测其可能的源位置。本文在四种不同纤维层的复合板和一块带孔的铝板中研究了用 T-TDM、I-TDM 和 GRNN-TDM 方法定位声发射源。定位结果表明，GRNN-TDM方法的定位精度高于T-TDM和I-TDM方法。神经网络模型利用从验证点收集的时间差数据预测其可能的源位置。本文在四种不同纤维层的复合板和一块带孔的铝板中研究了用 T-TDM、I-TDM 和 GRNN-TDM 方法定位声发射源。定位结果表明，GRNN-TDM方法的定位精度高于T-TDM和I-TDM方法。