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Table 5 Comparison with the classification results from the literature on the bearing dataset

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

Testing data size

Classification accuracy (%)

[85]

Raw Vibration data with entropic features.

10-fold cross-validation

98.9 ± 1.2

Compressed measurements of 0.5 sampling rate followed by signals recovery.

92.4 ± 0.5

Compressed sampled 0.25 sampling rate followed by signals recovery.

84.6 ± 3.4

[86]

CS

 

98.6 ± 0.3

CS-PCA

40%

98.5 ± 0.4

CS-LDA

 

89.8 ± 3.5

[87]

FMM-RF (SampEn + PS)

5-fold cross-validation

99.81 ± 0.41

[88]

SVM

10-fold cross-validation

93.5 ± 0.50

MLP

93.7 ± 0.21

[89]

GP generated feature sets (un-normalised)

50%

 

ANN

96.5

SVM

97.1

This paper

RGBVI-CNN (Our proposed method)

50%

99.8% ± 0.1

40%

99.9% ± 0.1

30%

100% ± 0.0