  Intelligent fault diagnosis has been increasingly improved with the evolution
of deep learning (DL) approaches. Recently, the emerging graph neural networks
(GNNs) have also been introduced in the field of fault diagnosis with the goal
to make better use of the inductive bias of the interdependencies between the
different sensor measurements. However, there are some limitations with these
GNN-based fault diagnosis methods. First, they lack the ability to realize
multiscale feature extraction due to the fixed receptive field of GNNs.
Secondly, they eventually encounter the over-smoothing problem with increase of
model depth. Lastly, the extracted features of these GNNs are hard to
understand owing to the black-box nature of GNNs. To address these issues, a
filter-informed spectral graph wavelet network (SGWN) is proposed in this
paper. In SGWN, the spectral graph wavelet convolutional (SGWConv) layer is
established upon the spectral graph wavelet transform, which can decompose a
graph signal into scaling function coefficients and spectral graph wavelet
coefficients. With the help of SGWConv, SGWN is able to prevent the
over-smoothing problem caused by long-range low-pass filtering, by
simultaneously extracting low-pass and band-pass features. Furthermore, to
speed up the computation of SGWN, the scaling kernel function and graph wavelet
kernel function in SGWConv are approximated by the Chebyshev polynomials. The
effectiveness of the proposed SGWN is evaluated on the collected solenoid valve
dataset and aero-engine intershaft bearing dataset. The experimental results
show that SGWN can outperform the comparative methods in both diagnostic
accuracy and the ability to prevent over-smoothing. Moreover, its extracted
features are also interpretable with domain knowledge.
