A Belief Network Reasoning Framework for Fault Localization in Communication Networks

Area of Science:

• Computer Science
• Network Engineering
• Artificial Intelligence

Background:

• Complex communication networks generate numerous alarms, complicating manual fault localization.
• Traditional fault localization relies on human operators, which is inefficient and prone to errors.

Purpose of the Study:

• To develop an autonomous and intelligent framework for efficient fault localization in communication networks.
• To automate the root cause analysis of network failures.

Main Methods:

• Utilizing a message propagation mechanism within belief networks for fault inference.
• Employing a polytree with a noisy OR-gate model (PTNORgate) to reduce computational complexity.
• Implementing a network parameter table for efficient data storage and retrieval.

Main Results:

• The proposed framework successfully identifies the root cause of network faults in an event-driven manner.
• The PTNORgate model significantly reduces computational complexity compared to traditional Bayesian networks.
• Case studies demonstrate the framework's speed and reliability in fault localization.

Conclusions:

• The developed framework offers an effective and automated solution for fault localization in communication networks.
• The approach enhances automation and improves the efficiency of network fault management.
• The method is suitable for real-world applications requiring fast and reliable fault identification.