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Table 7 A comparison with the classification results from the literature on bearing datasets A, B, and C datasets

From: Connected Components-based Colour Image Representations of Vibrations for a Two-stage Fault Diagnosis of Roller Bearings Using Convolutional Neural Networks

Reference No. Method Dataset Testing data size (%) Classification accuracy (%)
[34] DNN A 50 99.95 ± 0.06
B   99.61 ± 0.21
C   99.74 ± 0.16
BPNN A 50 65.2 ± 18.09
B   61.95 ± 22.09
C   69.82 ± 17.67
[36] CS-DNN with CS sampling rate of 0.1 A 50 99.3 ± 0.6
B 99.7 ± 0.5
C 100 ± 0.0
[91] MLP A 50 95.7
B 99.6
C 99.4
[67] AlexNet \(A\to B;A\to C;B\to A;\) 94.3
ResNet \(B\to C;C\to A;C\to B\) 94.6
ICN 97.2
This paper RGBVI-CNN *the proposed method A 50 99.6 ± 0.4
B 99.2 ± 0.7
C 99.3± 0.7
A 40 99.9 ± 0.1
B 99.8 ± 0.1
C 99.6± 0.3
A 30 100 ± 0.0
B 99.9 ± 0.1
C 99.9± 0.1