  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.
