Developments in Optical Fiber Network Fault Detection Methods: An Extensive Analysis

Abstract

The rapid development of transmissions media in computer networks has set optical fiber at the very front because of their high data transmission abilities and low constriction. However, guaranteeing the dependability and usefulness of optical fiber networks stays a critical test, particularly in recognizing and tending to issues expeditiously. This paper gives a careful examination of shortcoming discovery strategies in optical fiber networks, beginning with an investigation of issue types in view of the information from a neighborhood stations which are called Network Operations Centers, NOCs. It examines the meaning of issue identification, order, and their effect on network execution. Moreover, the paper investigates conventional shortcoming recognition techniques like Optical Time Area Reflectometer (OTDR) and their restrictions in pinpointing issue areas precisely. To overcome these difficulties, the paper investigates the coordination of AI (ML) procedures for issue of fault location and expectation in optical networks. Different utilizations of ML in issue discovery, including shortcoming area, prescient upkeep, oddity location, and enhancement of sign quality, are examined exhaustively. Also, late examination endeavors and their commitments to the field of issue location and characterization in optical networks are dissected. The paper finishes up by underscoring the capability of MLbased ways to deal with improve issue discovery effectiveness, further develop network dependability, and decrease margin time in optical fiber networks.