Fault Identification in Optical Transport Network Using CNN

Abstract

The growing diversity Regarding the provision of information and services  transmitted Using optical technology  transport networks (OTN) has made network survival a crucial issue in current study. Fault detection refers to the process of identifying faults that arise from various issues such as packet loss, disconnection, and others in the OTN. The determination of the fault location in an OTN is highly significant in the analysis of the resilience of optical networks. This study presents a fault diagnosis system that utilises a Convolutional Neural Network (CNN), focusing primarily on distinguishing between hard faults (HF) and soft faults (SF). The CNN is implemented in where the fault is situated domain of OTN to determine the presence or absence of potential fault locations. The notion An F-Measure is implemented in order to quantify the impact of positioning.utilising Location, time, mean squared error (MSE), and F-measure. The scientific investigation demonstrates that the suggested CNN neural network achieves the highest performance. The suggested CNN has a reduced localization time and achieves an F1-score of 0.98 after 85 iterations. This level of accuracy and real-time performance fulfils the requirements for fault identification. Hence, there is significant potential and practical utility in incorporating neural networks in the field of identifying and locating faults in optical transport networks.