Graph neural network-based fault diagnosis: a review

TL;DR

This review paper discusses the application of graph neural networks in fault diagnosis, highlighting their advantages, architectures, and recent experimental successes, while also exploring future research directions.

Contribution

It provides an accessible overview of GNN principles, reviews recent GNN-based fault diagnosis methods, and discusses future challenges and perspectives in the field.

Findings

GNN-based methods achieve high fault diagnosis accuracy.

Graph representations outperform traditional data formats.

Future research should address scalability and real-time implementation.

Abstract

Graph neural network (GNN)-based fault diagnosis (FD) has received increasing attention in recent years, due to the fact that data coming from several application domains can be advantageously represented as graphs. Indeed, this particular representation form has led to superior performance compared to traditional FD approaches. In this review, an easy introduction to GNN, potential applications to the field of fault diagnosis, and future perspectives are given. First, the paper reviews neural network-based FD methods by focusing on their data representations, namely, time-series, images, and graphs. Second, basic principles and principal architectures of GNN are introduced, with attention to graph convolutional networks, graph attention networks, graph sample and aggregate, graph auto-encoder, and spatial-temporal graph convolutional networks. Third, the most relevant fault diagnosis methods based on GNN are validated through the detailed experiments, and conclusions are made that the GNN-based methods can achieve good fault diagnosis performance. Finally, discussions and future challenges are provided.