Fault Detection and Isolation Methods in Subsea Observation Networks.

Area of Science:

• Marine Technology
• Deep-Sea Exploration
• Network Reliability Engineering

Background:

• Subsea observation networks are crucial for deep-sea exploration.
• Harsh undersea conditions significantly impact network reliability.
• Existing fault detection methods may not adequately address high-impedance and open-circuit faults in submarine cables.

Purpose of the Study:

• To develop and experimentally verify a deep learning-based system for detecting and predicting high-impedance and open-circuit faults in underwater observation networks.
• To investigate fault isolation strategies and communication protocols for subsea networks.
• To enhance the overall reliability and operational integrity of deep-sea exploration systems.

Main Methods:

• System modeling and simulation of underwater observation networks under various fault conditions.
• Collection and analysis of voltage and current data from operating nodes during simulated faults.
• Application of deep learning algorithms for supervised training and fault prediction.
• Laboratory-based construction and testing of a fault location system model.
• Design and evaluation of a fault isolation system focusing on communication protocols.

Main Results:

• The deep learning algorithm effectively detected and predicted high-impedance and open-circuit faults in submarine cables.
• Experimental results validated the accuracy and superiority of the proposed fault location system.
• The developed fault isolation system proved feasible and effective.
• The proposed methods demonstrated a significant improvement in the reliability of undersea observation network systems.

Conclusions:

• Deep learning offers a powerful tool for advanced fault detection and prediction in subsea networks.
• The integrated approach of fault detection, location, and isolation enhances the robustness of underwater observation systems.
• This research contributes to more reliable and efficient deep-sea exploration through improved network management.