Multiple attention mechanisms-driven component fault location in optical networks with network-wide monitoring data

Abstract

Fault location is an essential part of optical network operation and maintenance, and network operators have expectations to achieve highly accurate and precise fault location for reducing the cost of fault recovery. However, due to the scale of such networks, the volume of monitoring data (MD) is quite large, which poses a great challenge for fault location. An attention mechanism is an effective way to focus on the important information from massive input for the current task, which originates from the study of human vision. Targeting component fault location in optical networks, we propose an attention mechanism-based strategy, which consists of a sequence attention mechanism (SAT), a channel attention mechanism (CAT), a graph attention mechanism (GAT), and a fully connected neural network (FCNN). SAT, CAT, and GAT are applied for link, node, and network representation, respectively, taking corresponding MD as input. The FCNN is responsible for analyzing the correlation between MD and completing the fault location decision. All three attention mechanisms can filter out the more critical MD, assisting the FCNN to make more accurate decisions. We compare the performance of the proposed strategy and artificial neural networks (ANNs) in partial telemetry scenarios. Simulation results indicate that our strategy outperforms ANNs with respect to the accuracy of fault location by focusing on more critical MD and achieves a maximum improvement by 5.6%. Moreover, its feasibility with real data is verified on an experimental testbed consisting of hybrid optical-electrical switching nodes. Extensive results show that our strategy has the potential to achieve highly accurate fault location in real networks.