Artificial Intelligence-Driven Predictive Maintenance for Optical Fiber Networks

Predictive maintenance solutions need to be developed strongly because optical fiber networks are becoming more complex which leads to reliability issues and downtime requirements. An Artificial Intelligence framework specifically designed to anticipate and prevent optical fiber network failures depends on SVMs together with wrapper-based feature selection and time-series data transformation procedures. The extraction of relevant statistical and temporal patterns is performed on time-series data retrieved from network telemetry instrumentation consisting of signal attenuation together with noise measurements. Multiple features of importance are identified through the combination of SVMs with wrapper-based methods that enable interpretable models with reduced computational requirements. The SVM classifier develops its ability to detect potential network faults using these optimized characteristics during training. The proposed framework demonstrates superior performance than typical approaches while using actual network data for testing. The research demonstrates efficient performance of improved feature selection together with support vector machines (SVMs) in predicting damage to optical fibers through effective data processing which helps network operators to establish dependable AI-controlled systems for fiber network monitoring.