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