Citation: Wang, H.; Pu, L. Bearing
Fault Diagnosis of Split Attention
Network Based on Deep Subdomain
Adaptation. Appl. Sci. 2022, 12, 12762.
https://doi.org/10.3390/
app122412762
Academic Editor: Ki-Yong Oh
Received: 12 October 2022
Accepted: 10 December 2022
Published: 12 December 2022
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Article
Bearing Fault Diagnosis of Split Attention Network Based on
Deep Subdomain Adaptation
Haitao Wang * and Lindong Pu
Institute of Mechanical Dynamics Theory and Application, Xi’an University of Architecture and Technology,
Xi’an 710055, China
* Correspondence: wanghaitao@xauat.edu.cn
Abstract:
The insufficient learning ability of traditional convolutional neural network for key fault
features, as well as the characteristic distribution of vibration data of rolling bearing collected under
variable working conditions is inconsistent, and decreases the bearing fault diagnosis accuracy. To
address the problem, a deep subdomain adaptation split attention network (SPDSAN) is proposed
for intelligent fault diagnosis of bearings. Firstly, the time-frequency diagram of a vibration signal is
obtained by the continuous wavelet transform to show the time-frequency characteristics. Secondly,
a residual split-attention network (ResNeSt) that integrates multi-path and channel attention mecha-
nisms is constructed to extract the key features of rolling bearings to prevent feature loss. Then, a
subdomain adaptation layer is added to ResNeSt to align the distribution of related subdomain data
by minimizing the local maximum mean difference. Finally, the SPDSAN model is validated using
the Case Western Reserve University datasets. The results show that the average diagnostic accuracy
of the proposed method is 99.9% when the test set samples are not labeled, which is higher compared
to the accuracy of other mainstream intelligent fault diagnosis models.
Keywords: subdomain adaptive; split attention; transfer learning; fault diagnosis
1. Introduction
Rotating machinery is widely used in aerospace, automobile manufacturing, wind
power generation and other important engineering fields. Rolling bearing is a key com-
ponent in rotating machinery. Because this mechanical equipment often operates under
complex working conditions, bearings are prone to pitting, breaking, gluing and other
failures, which will lead to the paralysis of the mechanical equipment and cause significant
economic losses [
1
]. Statistical analysis results provided by multiple studies have shown
that more than 40% of the equipment faults are related to bearings [
2
]. Thus, how to
improve the fault diagnosis of bearings under variable working conditions is related to the
stable operation of the whole equipment and production line.
The traditional fault diagnosis method determines the equipment health state by
establishing the corresponding dynamic model. For instance, Ambrokiewicz et al. [
3
] not
only considered the bearing internal stiffness, damping, clearance and other nonlinear
characteristics, but also took the bearing external load, eccentricity and other characteristics
as factors affecting the normal operation of the bearing ball. The dynamics model of
the ball bearing motion process with two degrees of freedom was established to reveal
the dimensionless relationship and the influence on the system response. In the study
by Huangfu et al. [
4
], the traditional loaded tooth contact analysis (LTCA) method was
extended to calculate the mesh stiffness and contact stress of spalled gear pairs, and
established a novel dynamic model for spalled gear pairs to describe the dynamic response
of the gear pair under different spall modes. Such methods heavily rely on the researcher
expertise, and specific devices are needed to establish specific dynamic models, greatly
limiting their applicability [5].
Appl. Sci. 2022, 12, 12762. https://doi.org/10.3390/app122412762 https://www.mdpi.com/journal/applsci
