Research Article
Deep Transfer Learning Method Based on 1D-CNN for Bearing Fault Diagnosis
Table 6
Classification accuracy of various methods.
| | Method | E ⟶ F | F ⟶ E | E ⟶ G | G ⟶ E | F ⟶ G | G ⟶ F | AVG (%) |
| | CNN | 62.8 ± 0.1% | 51.4 ± 0.43% | 53.33 ± 1.26% | 62.4 ± 2.13% | 55.4 ± 0.28% | 56.67 ± 0.26% | 57.00 | | TCA [22] | 39.13 ± 3.17% | 37.33 ± 2.22% | 32.93 ± 1.13% | 33.25 ± 1.24% | 42.80 ± 0.71% | 38.27 ± 1.52% | 37.29 | | CORAL [31] | 41.55 ± 2.16% | 36.33 ± 4.22% | 35.80 ± 3.52% | 36.86 ± 7.14% | 37.53 ± 1.93% | 40.47 ± 2.18% | 38.09 | | WD-DTL [35] | 77.52 ± 3.09% | 74.80 ± 2.10% | 64.69 ± 2.99% | 72.86 ± 2.61% | 75.35 ± 2.59% | 68.74 ± 4.27% | 72.23 | | DDC [36] | 71.67 ± 4.25% | 73.60 ± 4.43% | 66.70 ± 2.32% | 69.87 ± 4.12% | 73.80 ± 2.58% | 72.07 ± 2.21% | 71.29 | | DAN [37] | 72.90 ± 2.17% | 76.33 ± 3.32% | 67.67 ± 3.59% | 66.40 ± 4.68% | 74.40 ± 3.63% | 72.30 ± 2.22% | 71.67 | | The proposed | 75.50 ± 3.11% | 81.33 ± 2.06% | 75.07 ± 3.52% | 75.40 ± 0.46% | 80.53 ± 3.14% | 71.67 ± 4.32% | 76.58 |
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