Remaining Useful Life Estimation Using Deep Convolutional Generative Adversarial Networks Based on an Autoencoder Scheme

Algorithm 1

Outline of proposed model training for RUL estimation.

	Phase 1 DCGAN based on AE Modeling
	Input: sliding window training data
	Initialize: CNN layer parameters, batch size, learning rate
	repeat
	Generation losses = generator loss + reconstruction loss of AE
	Discrimination loss = discriminator error of real data + discriminator error of
	generative data
	Update Generator and Discriminator parameters using RMSprop optimizer separately
	until Maximum iterations
	return Trained DCGAN-AE model
	end
	Phase 2 Supervised Learning Stage
	Input: training data and label RUL
	Initialize: LSTM layer parameters, FNN layer parameters, dropout rate
	repeat
	Extracted representations Pretraining DCGAN-AE model
	Conducting LSTM operations with the representations (dropout rate is
	employed to avoid the overfitting problem)
	FNN is used for RUL estimation
	Compute losses between predicted RUL with label RUL
	Update parameters using Adam
	until Maximum iterations
	return Trained RUL prediction model
	end