Research Article
A New Deep Convolutional Domain Adaptation Network for Bearing Fault Diagnosis under Different Working Conditions
Table 2
Parameter set of the network.
| Layers | Parameters | Activation functions | Output size |
| Input | — | — | 28 × 28 | C1 | Kernels: 3 × 3 × 8, bias: 8 × 1 | ReLU | 28 × 28 × 8 | C2 | Kernels: 3 × 3 × 16, bias: 16 × 1 | ReLU | 28 × 28 × 16 | P2 | Stride: 2 | — | 14 × 14 × 16 | C3 | Kernels: 3 × 3 × 32, bias: 32 × 1 | ReLU | 14 × 14 × 32 | C4 | Kernels: 3 × 3 × 64, bias: 64 × 1 | ReLU | 14 × 14 × 64 | P4 | Stride: 2 | — | 7 × 7 × 64 | F1 | Weights: 3136 × 256, bias: 256 × 1 | — | 256 × 1 | F2 | Weights: 256 × 128, bias: 128 × 1 | ReLU | 128 × 1 | F3 | Weights: 128 × 10, bias: 10 × 1 | ReLU | 10 × 1 | F4 | Weights: 3136 × 256, bias: 256 × 1 | ReLU | 256 × 1 | F5 | Weights: 256 × 2, bias: 2 × 1 | ReLU | 2 × 1 |
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