Failure of buried pipelines can result in serious impacts, such as explosions, environmental contamination, and economic losses. Early detection and location of unexpected events is crucial to prevent such events. However, conventional monitoring methods exhibit limited generalization performance under varying environmental and operational conditions. Furthermore, the cross‐correlation‐based time difference of arrival approach, which is widely used for source localization, also lacks the capability to identify anomalous events. This study introduces what is termed as the two‐stream global fusion classifier (TSGFC), a novel multitask deep‐learning model designed to early detection and location of unexpected events in buried pipelines, even under previously unseen conditions. TSGFC combines spatial and temporal features from accelerometer data using a global fusion mechanism, and uniquely performs both event classification and source localization through a unified multitask framework. To ensure generalization across diverse environments, we employed a unique data acquisition strategy that was specifically designed to evaluate the model's performance under domain shift by using training data from controlled experiments and test data from real‐world excavation activities conducted on a completely different pipeline. Our results confirm that TSGFC can identify unexpected excavation activity with 95.45% accuracy and minimal false alarms, even when evaluated on data collected from a completely different buried pipeline under real‐world excavation scenarios unseen during training.

中文翻译：

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使用双流全局融合分类器模型，在看不见的条件下及早检测和定位埋地管道中的意外事件

埋地管道故障会导致爆炸、环境污染和经济损失等严重影响。及早发现和定位意外事件对于防止此类事件至关重要。然而，传统的监测方法在不同的环境和作条件下表现出有限的泛化性能。此外，广泛用于源定位的基于互相关的到达时间差方法也缺乏识别异常事件的能力。本研究介绍了所谓的双流全局融合分类器 （TSGFC），这是一种新颖的多任务深度学习模型，旨在及早检测和定位埋地管道中的意外事件，即使在以前从未见过的条件下也是如此。TSGFC 使用全局融合机制将加速度计数据的空间和时间特征相结合，并通过统一的多任务框架独特地执行事件分类和源定位。为了确保跨不同环境的泛化，我们采用了一种独特的数据采集策略，该策略专门设计用于通过使用来自受控实验的训练数据和来自在完全不同的管道上进行的真实挖掘活动的测试数据来评估模型在域偏移下的性能。我们的结果证实，TSGFC 可以以 95.45% 的准确率和最小的误报识别意外的挖掘活动，即使在训练期间看不到的真实挖掘场景中从完全不同的埋地管道收集的数据进行评估也是如此。