  Early detection of faults is of importance to avoid catastrophic accidents
and ensure safe operation of machinery. A novel graph neural network-based
fault detection method is proposed to build a bridge between AI and real-world
running mechanical systems. First, the vibration signals, which are Euclidean
structured data, are converted into graph (non-Euclidean structured data), so
that the vibration signals, which are originally independent of each other, are
correlated with each other. Second, inputs the dataset together with its
corresponding graph into the GNN for training, which contains graphs in each
hidden layer of the network, enabling the graph neural network to learn the
feature values of itself and its neighbors, and the obtained early features
have stronger discriminability. Finally, determines the top-n objects that are
difficult to reconstruct in the output layer of the GNN as fault objects. A
public datasets of bearings have been used to verify the effectiveness of the
proposed method. We find that the proposed method can successfully detect
faulty objects that are mixed in the normal object region.
