Mitigating Environmental Impacts on Optical Fiber Fault Detection Using Machine Learning

Fiber optic networks are integral to modern communication systems, offering high-speed data transmission over long distances. However, the performance of these networks can be significantly affected by environmental factors such as temperature, humidity, pressure, and mechanical stress. These factors can alter the physical properties of optical fibers, leading to signal degradation and potentially causing errors in fault detection. This paper investigates the impact of environmental factors on fiber fault detection systems, particularly those utilizing machine learning algorithms. We propose methods to mitigate these effects and enhance detection accuracy. The research is validated through simulations and case studies, providing practical guidelines for deploying fiber fault detection systems in various environmental conditions.