Remaining Useful Life Prediction Techniques of Electric Valves for Nuclear Power Plants with Convolution Kernel and LSTM

Table 4

The architecture definition of LSTM.

Name	Definition	Value
Network structure	Convolution kernel + LSTM network	None
Sliding window size	Size of sliding window for data preprocessing	40
Normalization mode	Normalization of data	Z-score
Layers of convolution kernel	Layers of convolution kernel	3
Number of convolution kernels	Number of convolution kernels in each layer	64
Convolution kernel size	Kernel size of convolution calculation	3 × 3
Pooling size	Size of average pooling	2 × 2
LSTM_size	LSTM layer unit	256
num_layer	Number of stacked LSTM layers	2
Keep_prob	Percentage retained in dropout operations	0.99
Init_learning_rate	Initial learning rate	0.01
Init_epoch	Iterations using the initial learning rate	5
Max_epoch	Total training times	200
Batch_size	The amount of data used in small batches	64
Loss function	None	MSE
Optimization method	Optimization algorithms for backpropagation	SGD