Rotating Machinery Remaining Useful Life Prediction Scheme Using Deep-Learning-Based Health Indicator and a New RVM

Table 4

Parameters of hybrid deep learning network.

Network layer	Parameters
	Input picture size	Number of channels	Convolution kernel size	Step size	Number of nodes
Input layer	[100 × 100]	3	—	—	—
Convolutional layer 1	—	96	[11 × 11]	4	—
Pooling layer 1	—	96	[3 × 3]	2	—
Convolutional layer 2	—	256	[5 × 5]	1	—
Pooling layer 2	—	256	[3 × 3]	2	—
Convolutional layer 3	—	384	[3 × 3]	1	—
Convolutional layer 4	—	384	[3 × 3]	1	—
Convolutional layer 5	—	256	[3 × 3]	1	—
Pooling layer 5	—	256	[3 × 3]	2	—
CNN flatten layer	—	7424 (6400 + 1024)	—	—	—
LSTM layer 1	—		—	—	80
LSTM layer 2	—	—	—	—	60
LSTM layer 3	—	—	—	—	30
LSTM flatten layer	—	—	—	—	30
CNN + LSTM connected layer		—	—	—	7454 (7424 + 30)
Fully connected layer 1	—	4096	—	—	—
Fully connected layer 2	—	1000	—	—	—
Output layer	—	1	—	—	—