A Mixture of Experts Model for Third-Party Pipeline Intrusion Detection Using DAS.

Area of Science:

• Pipeline safety engineering
• Sensor technology
• Signal processing

Background:

• Distributed acoustic sensing (DAS) faces challenges in accuracy, real-time performance, and weak signal detection for pipeline third-party intrusion (TPI).
• Existing DAS systems struggle in complex environments, limiting their effectiveness in pipeline monitoring.

Purpose of the Study:

• To propose an innovative Pipeline Fiber Optic Warning-Mixture of Experts (PFOW-MoE) method to address limitations in DAS for pipeline safety.
• To enhance recognition accuracy, real-time performance, and weak signal identification for TPI detection.

Main Methods:

• Developed a multi-modal feature perception expert model considering time, spatial, and frequency domains of intrusion behaviors.
• Implemented an efficient decision framework with a dynamic gating mechanism for real-time signal evaluation.
• Integrated a robustness enhancement mechanism with a weak signal detection branch into the dynamic gating.

Main Results:

• The PFOW-MoE method achieved 98.27% accuracy on the entire dataset of actual pipeline intrusion samples.
• Achieved 96.00% accuracy specifically on weak signal samples, demonstrating robustness.
• Attained a single-sample inference time of 0.78 ms, meeting practical real-time engineering requirements.

Conclusions:

• The proposed PFOW-MoE method effectively addresses key challenges in DAS for pipeline safety warning systems.
• The method demonstrates superior performance in accuracy, real-time processing, and detection of weak signals for TPI.
• PFOW-MoE offers a robust and efficient solution for real-time pipeline monitoring and intrusion detection.