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A Bearing Fault Diagnosis Model based on Minimum Average Composite Entropy and Parallel Attention Mechanism Convolutional Neural Network Cover

A Bearing Fault Diagnosis Model based on Minimum Average Composite Entropy and Parallel Attention Mechanism Convolutional Neural Network

Open Access
|Aug 2025

Figures & Tables

Fig. 1.

Research block diagram.
Research block diagram.

Fig. 2.

Sk, Sr, Re schematic diagram of variation with defects.
Sk, Sr, Re schematic diagram of variation with defects.

Fig. 3.

Sk, Sr, Re sensitivity to accidental noise.
Sk, Sr, Re sensitivity to accidental noise.

Fig. 4.

Schematic diagram of the variation with defects.
Schematic diagram of the variation with defects.

Fig. 5.

Schematic diagram of the channel attention mechanism.
Schematic diagram of the channel attention mechanism.

Fig. 6.

Schematic diagram of the spatial attention mechanism.
Schematic diagram of the spatial attention mechanism.

Fig. 7.

MACE + PFACNN model structure diagram.
MACE + PFACNN model structure diagram.

Fig. 8.

Time domain waveform of outer ring signal.
Time domain waveform of outer ring signal.

Fig. 9.

Fitness function.
Fitness function.

Fig. 10.

Entropy change curve.
Entropy change curve.

Fig. 11.

Analysis results after MACE-VMD.
Analysis results after MACE-VMD.

Fig. 12.

Analysis results of noise resistance.
Analysis results of noise resistance.

Fig. 13.

Analysis results of the ability to resist complex noises.
Analysis results of the ability to resist complex noises.

Fig. 14.

Analysis results of the generalization ability.
Analysis results of the generalization ability.

Fig. 15.

Experimental layout.1. Motor, 2. Coupling, 3. Acceleration sensor, 4. Bearing housing I, 5. Spindle, 6. Rotor, 7. Acceleration sensor, 8. Bearing housing II, 9. Bearing I, 10. Bearing II.
Experimental layout.1. Motor, 2. Coupling, 3. Acceleration sensor, 4. Bearing housing I, 5. Spindle, 6. Rotor, 7. Acceleration sensor, 8. Bearing housing II, 9. Bearing I, 10. Bearing II.

Fig. 16.

Fault diagnosis results.
Fault diagnosis results.

Fig. 17.

Visualization results after diagnosis.
Visualization results after diagnosis.

Fig. 18.

Analysis results of generalization ability.
Analysis results of generalization ability.

Dataset classification_

Fault locationFailure diameter [mm]TagDataset
Training setTest set
Regular——17030
Inner ring——27030
Outer ring90°3A7030
135°3B7030
Regular——47030
Compound failureouter 90°5A7030
outer 135°5B7030

Experimental results of 5 dB complex noise comparison_

ModelMACE + PFACNNRVMD+ DCNNRVMD+ CNNE+CNN BiGRUE+CNN SVM
Accuracy [%]91.380.152.154.248.3
Recall rate [%]91.680.552.754.948.8
F1 [%]91.480.352.554.648.6

Results of ablation experiment_

Module
ModelAccuracy [%]
123
××A91.2
×B93.7
×C97.4
×D95.3
E98.9

Experimental parameters_

Inner diameter [mm]Pitch diameter [mm]Thickness [mm]Outer diameter [mm]Rolling diameter [mm]Contact angle [°]
2539155280

Fault diagnosis results_

CategoryAccuracy [%]CategoryAccuracy [%]
11006100
21007100
3100899.3
4100999.3
510010100

Results of 0 dB white noise comparison test_

ModelMACE + PFACNNIF+ CNNM+ CNNE+CNNE+ CNNSVM
Accuracy [%]89.264.551.667.872.4
Recall rate [%]89.864.952.168.272.9
F1 [%]89.764.651.867.772.6

Generalization experiment results – Accuracy [%]_

ModelMACE + PFACNNE+CNNE+SVMIF+CNNM+ DCNN
3A-3B97.9891.1491.1289.9990.11
3B-3A93.6492.1388.9690.9689.11
5A-5B93.8790.1189.1386.5790.40
5B-5A92.0189.4190.1188.7689.13
Mean94.3790.6989.8389.0789.68
Language: English
Page range: 178 - 189
Submitted on: Feb 28, 2025
|
Accepted on: May 27, 2025
|
Published on: Aug 14, 2025
In partnership with: Paradigm Publishing Services
Publication frequency: Volume open

© 2025 Zhen Zhang, Shixi Yang, Jun He, Wanchun Zhou, Yanxu Liu, published by Slovak Academy of Sciences, Institute of Measurement Science
This work is licensed under the Creative Commons Attribution-NonCommercial 4.0 License.