Fault identification of optical communication network based on machine learning

In order to accurately diagnose optical communication system faults and ensure the stable operation of optical communication system, the fault diagnosis method of optical communication system based on Gaussian weighted KNN is studied. This method is developed with the aim of enhancing the precision and reliability of fault detection within optical networks. By leveraging the Gaussian weighted KNN algorithm, which is a machine learning technique, the system is able to classify optical communication faults more effectively. The Gaussian weighted KNN algorithm is particularly useful in this context because it assigns weights to the neighboring data points based on their distance from the query point, which in this case is the fault to be diagnosed. This allows the algorithm to give more importance to the faults that are more similar to the one being analyzed, thus improving the accuracy of the classification process. Based on the analysis of the module composition of optical communication system, Gaussian weighted KNN algorithm is used to classify optical communication faults. This involves breaking down the optical communication system into its constituent modules and analyzing each module’s role and potential points of failure. By understanding the system at this granular level, the fault diagnosis method can be tailored to the specific characteristics of each module, thereby enhancing the overall diagnostic capability. Experimental results show that the accuracy of this method is 98.92%. This high level of accuracy indicates that the Gaussian weighted KNN-based fault diagnosis method is highly effective in identifying and classifying faults within optical communication systems.