This work considers the problem of fault localization in transparent optical networks. The aim is to localize singlelink failures by utilizing statistical machine learning techniques trained on data that describe the network state upon current and past failure incidents. In particular, a Gaussian Process (GP) classifier is trained on historical data extracted from the examined network, with the goal of modeling and predicting the failure probability of each link therein. To limit the set of suspect links for every failure incident, the proposed approach is complemented with the utilization of a Graph-Based Correlation heuristic. The proposed approach is tested on a dataset generated for an OFDM-based optical network, demonstrating that it achieves a high localization accuracy. The proposed scheme can be used by service providers for reducing the Mean-Time-To- Repair of the failure.

© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. T. Panayiotou, S. P. Chatzis and G. Ellinas, "A probabilistic approach for failure localization," 2017 International Conference on Optical Network Design and Modeling (ONDM), Budapest, 2017, pp. 1-6.