Research Article
Deep Transfer Learning Method Based on 1D-CNN for Bearing Fault Diagnosis
Table 1
Structure and parameters of CNN.
| Layers | Parameters | Activation function |
| Input | — | — | Conv1 | Kernels: 1 × 64 × 16, stride: 16 | ReLU | Pool1 | Tride: 2, max pooling | — | Conv2 | Kernels: 1 × 3 × 32, stride: 1 | ReLU | Conv3 | Kernels: 1 × 5 × 64, stride: 1 | ReLU | Conv4 | Kernels: 1 × 5 × 128, stride: 1 | ReLU | Pool2 | Tride:2, max pooling | — | FC1 | Weights: 5000 | ReLU | FC2 | Weights: 1000 | ReLU | Output | Weights: 10 | Softmax |
|
|