Deep Transfer Learning Method Based on 1D-CNN for Bearing Fault Diagnosis

Table 5

Classification accuracy of various methods.

Method

A ⟶ B

B ⟶ A

A ⟶ C

C ⟶ A

A ⟶ D

D ⟶ A

B ⟶ C

C ⟶ B

B ⟶ D

D ⟶ B

C ⟶ D

D ⟶ C

AVG

CNN

95.80 ± 0.1%

95.67 ± 0.43%

93.33 ± 1.26%

92.40 ± 2.13%

95.33 ± 0.17%

77.60 ± 1.17%

95.40 ± 0.28%

96.67 ± 0.26%

91.07 ± 0.47%

77.27 ± 5.35%

97.13 ± 0.12%

75.20 ± 4.22%

90.24

TCA [22]

79.13 ± 3.17%

77.33 ± 2.22%

72.93 ± 1.13%

63.00 ± 1.24%

66.67 ± 4.73%

74.47 ± 0.62%

82.80 ± 0.71%

78.27 ± 1.52%

75.27 ± 3.53%

74.13 ± 1.22%

61.07 ± 4.24%

68.44 ± 5.36%

72.79

CORAL [31]

75.55 ± 2.16%

72.33 ± 4.22%

69.80 ± 3.52%

66.86 ± 7.14%

52.73 ± 4.15%

54.00 ± 5.56%

72.53 ± 1.93%

70.47 ± 2.18%

80.86 ± 0.19%

76.93 ± 3.17%

72.27 ± 4.16%

67.60 ± 5.13%

67.60

WD-DTL [35]

97.52 ± 3.09%

96.80 ± 1.10%

94.43 ± 2.99%

92.16 ± 2.61%

95.05 ± 2.52%

89.82 ± 2.41%

99.69 ± 0.59%

96.03 ± 6.27%

95.51 ± 2.52%

95.16 ± 3.67%

97.56 ± 3.31%

99.62 ± 0.80%

95.75

DDC [36]

95.60 ± 1.25%

90.60 ± 2.43%

95.00 ± 2.32%

97.87 ± 0.12%

86.73 ± 2.71%

88.47 ± 2.04%

92.80 ± 1.58%

97.07 ± 0.21%

89.53 ± 4.19%

79.27 ± 0.23%

96.87 ± 0.18%

86.27 ± 2.26%

91.34

DAN [37]

95.90 ± 1.17%

93.33 ± 1.32%

96.67 ± 0.59%

96.00 ± 0.68%

88.93 ± 4.66%

88.00 ± 1.02%

94.40 ± 1.63%

98.00 ± 0.22%

90.80 ± 1.13%

78.33 ± 3.16%

97.13 ± 0.33%

81.93 ± 3.42%

91.63

The proposed

98.50 ± 0.11%

98.33 ± 0.06%

99.07 ± 0.12%

98.40 ± 0.46%

99.00 ± 0.21%

93.87 ± 1.46%

99.53 ± 0.14%

97.67 ± 0.32%

98.40 ± 0.18%

95.20 ± 2.24%

98.93 ± 0.13%

97.20 ± 1.02%

97.85