Early detection and location of unexpected events in buried pipelines under unseen conditions using the two‐stream global fusion classifier model

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.