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