Research Article
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 |
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