Dual-Domain Fusion Network for Multi-Event Recognition in Φ-OTDR Sensing Systems

Abstract

Leveraging advances in artificial intelligence algorithms, distributed acoustic sensing (DAS) based on phase-sensitive optical time-domain reflectometry (Φ-OTDR) has achieved high event-recognition accuracy through a variety of learning models. Nevertheless, further improving the accuracy of multi-event recognition remains a persistent challenge. In this paper, we propose a Dual-Domain Fusion Network (DD-FusNet) for vibration event recognition in Φ-OTDR sensing systems. To fully capture signal dynamics, the model simultaneously processes time- and frequency-domain representations, employing a crucial cross-attention mechanism to bridge these branches and enable dynamic, learnable interactions. Experimental results based on a six-class field engineering vibration event dataset collected by Φ-OTDR, containing car events, manual tapping, road breaker, excavation, leaking and noise, demonstrate that the proposed method achieves an average accuracy of 99.12%, significantly outperforming baseline methods by approximately 3 to 10 percentage points in accuracy, thereby ensuring the accuracy of multi-event recognition. We believe the proposed DD-FusNet will advance the recognition capabilities of Φ-OTDR systems in complex industrial sensing applications.