Citation: Cui, J.; Zhong, Q.; Zheng, S.;
Peng, L.; Wen, J. A Lightweight
Model for Bearing Fault Diagnosis
Based on Gramian Angular Field and
Coordinate Attention. Machines 2022,
10, 282. https://doi.org/10.3390/
machines10040282
Academic Editors: Kelvin K.L. Wong,
Dhanjoo N. Ghista, Andrew W.H. Ip
and Wenjun (Chris) Zhang
Received: 18 March 2022
Accepted: 11 April 2022
Published: 17 April 2022
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Article
A Lightweight Model for Bearing Fault Diagnosis Based on
Gramian Angular Field and Coordinate Attention
Jialiang Cui , Qianwen Zhong * , Shubin Zheng *, Lele Peng and Jing Wen
School of Urban Railway Transportation, Shanghai University of Engineering Science, Shanghai 201620, China;
sycuijialiang@icloud.com (J.C.); lele.peng@sues.edu.cn (L.P.); wenjing_jlu@126.com (J.W.)
* Correspondence: qianwen.zhong@sues.edu.cn (Q.Z.); shubin.zheng@sues.edu.cn (S.Z.)
Abstract:
The key to ensuring rotating machinery’s safe and reliable operation is efficient and accurate
faults diagnosis. Intelligent fault diagnosis technology based on deep learning (DL) has gained
increasing attention. A critical challenge is how to embed the characteristics of time series into DL to
obtain stable features that correlate with equipment conditions. This study proposes a lightweight
rolling bearing fault diagnosis method based on Gramian angular field (GAF) and coordinated
attention (CA) to improve rolling bearing recognition performance and diagnosis efficiency. Firstly,
the time domain signal is encoded into GAF images after downsampling and segmentation. This
method retains the temporal relation of the time series and provides valuable features for DL.
Secondly, a lightweight convolution neural network (CNN) model is constructed through depthwise
separable convolution, inverse residual block, and linear bottleneck layer to learn advanced features.
After that, CA is employed to capture the long-range dependencies and identify the precise position
information of the GAF images with nearly no additional computational overhead. The proposed
method is tested and evaluated by CWRU bearing dataset and experimental dataset. The results
demonstrate that the CNN based on GAF and CA (GAF-CA-CNN) model can effectively reduce the
calculation overhead of the model and achieve high diagnostic accuracy.
Keywords:
rolling bearing fault diagnosis; lightweight neural network; downsampling; gramian
angular field; coordinate attention
1. Introduction
Rolling bearing is the critical component of rotating machinery and is widely used in
rail transit, precision machine tools, aerospace, and other fields. Due to the constant impact
of load, the rolling bearings are prone to cracks and pitting [
1
–
3
], which seriously affects
equipment operation and even cause safety accidents and economic losses. Therefore, it
is necessary to monitor the status of rolling bearings to ensure the regular operation of
mechanical equipment.
The collision between the matching surface and the damage of the rolling bearing
will produce a transient impact. If the rotation speed remains constant, it will produce
periodic transient impact. Additionally, rolling bearings of different fault types have their
specific vibration characteristics. Therefore, the fault diagnosis methods of rolling bearings
are primarily based on the processing and analysis of vibration signals. The existing fault
diagnosis methods for rolling bearings include signal processing-based and intelligent
diagnosis methods. The former can effectively extract fault features from the original
vibration signal, such as wavelet transform (WT) [
4
], empirical mode decomposition
(EMD) [
5
], and variational mode decomposition (VMD) [
6
]. Li et al. [
7
] used an improved
adaptive parameterless empirical wavelet transform (IAPEWT) for rolling bearing fault
diagnosis. In 2019, Chen et al. [
8
] presented a rolling bearing fault diagnosis method based
on EMD and sample quantile permutation entropy (SQPE). In 2020, Li et al. [
9
] designed a
rolling bearing diagnosis model based on VMD and fractional Fourier transform (FRFT).
Machines 2022, 10, 282. https://doi.org/10.3390/machines10040282 https://www.mdpi.com/journal/machines
