This study presents an innovative method for accurately picking the first-wave arrival time in acoustic emission (AE) localization, particularly effective in environments with low or variable signal-to-noise ratios (SNR). Utilizing an ensemble learning model, it synergizes multiple automatic arrival time estimation algorithms to enhance both consistency and robustness. The model, rooted in decision tree methodologies, integrates a variety of techniques, including Akaike information criterion (AIC), improved AIC, Hinkley, energy ratios, and wavelet transformation-based binary map. Its efficacy is demonstrated through testing on 549 manually annotated AE datasets, where the model’s predictions were benchmarked against manual picks, showcasing superior accuracy and robustness in AE event source localization. Evaluations of the model’s performance across various ensemble machine learning models highlighted its ability to significantly diminish localization errors, achieving an average absolute error of less than 1.5 mm. The study also delved into the impact of base pickers and the size of the training dataset on the model’s predictions. Findings indicated consistent performance across different decision tree models, with the accuracy of base pickers and training set size playing a significant role in outcomes. This research culminates in a decision tree-based ensemble machine learning solution that effectively estimates AE first-wave arrival times with high accuracy and robustness, even amidst fluctuating SNR conditions. Its adaptability and interpretability greatly reduce source localization errors in practical AE monitoring, effectively overcoming challenges associated with imprecise arrival time picking.

中文翻译：

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使用集成机器学习模型可靠地拾取声发射的到达时间

这项研究提出了一种创新方法，可在声发射 (AE) 定位中准确选取第一波到达时间，在信噪比 (SNR) 较低或可变的环境中特别有效。它利用集成学习模型，协同多种自动到达时间估计算法，以增强一致性和鲁棒性。该模型植根于决策树方法论，集成了多种技术，包括 Akaike 信息准则 (AIC)、改进的 AIC、Hinkley、能量比和基于小波变换的二值图。通过对 549 个手动注释的 AE 数据集进行测试证明了其功效，其中模型的预测以手动选择为基准，展示了 AE 事件源定位的卓越准确性和鲁棒性。对各种集成机器学习模型的模型性能评估强调了其显着减少定位误差的能力，实现了小于 1.5 毫米的平均绝对误差。该研究还深入研究了碱基选择器和训练数据集的大小对模型预测的影响。研究结果表明，不同决策树模型的性能一致，碱基选择器的准确性和训练集大小在结果中发挥着重要作用。这项研究最终形成了基于决策树的集成机器学习解决方案，即使在信噪比波动的情况下，该解决方案也能以高精度和鲁棒性有效地估计 AE 第一波到达时间。其适应性和可解释性极大地减少了实际AE监测中的源定位误差，有效克服了与不精确的到达时间拾取相关的挑战。