An interpretable graph neural network inspired by graph signal processing for mechanical fault diagnosis
As mechanical equipment grows more complex, multi-sensor monitoring and data fusion are essential for accurate fault diagnosis. While graph neural networks (GNNs) are popularly used in this field, their performance evaluations remain largely empirical, lacking a unified theoretical framework to explain and assess the...