Recurrent Graph Transformer Network for Multiple Fault Localization in Naval Shipboard Systems

TL;DR

This paper introduces a novel recurrent graph transformer network that improves fault localization accuracy in complex naval shipboard MVDC systems by capturing temporal and spatial features effectively.

Contribution

It presents a new deep graph neural network architecture combining gated recurrent units and multi-head attention for multi-fault diagnosis in naval systems.

Findings

Achieved 1-4% higher fault localization accuracy

Effectively identifies multiple successive faults

Validated on real shipboard MVDC system

Abstract

The integration of power electronics building blocks in modern MVDC 12kV Naval ship systems enhances energy management and functionality but also introduces complex fault detection and control challenges. These challenges strain traditional fault diagnostic methods, making it difficult to detect and manage faults across multiple locations while maintaining system stability and performance. This paper proposes a temporal recurrent graph transformer network for fault diagnosis in naval MVDC 12kV shipboard systems. The deep graph neural network uses gated recurrent units to capture temporal features and a multi-head attention mechanism to extract spatial features, enhancing diagnostic accuracy. The approach effectively identifies and evaluates successive multiple faults with high precision. The method is implemented and validated on the MVDC 12kV shipboard system designed by the ESDRC team, incorporating all key components. Results show significant improvements in fault localization accuracy, with a 1-4% increase in performance metrics compared to other machine learning methods.