Sequential Network with Residual Neural Network for Rotatory Machine Remaining Useful Life Prediction Using Deep Transfer Learning

Algorithm 1

The RUL algorithm for training.

	RUL algorithm for training is defined as follows:
	Input: Historical run-to-fail sensors data of rotatory machine
	For do:
	j is size of the time window
	Convert time-series sample into grey image
	Duplicate and extend channel into 3
	The whole datasets
	end
	Define corresponding RUL value
	Function TRAIN (X, N, d):
	t = [t − Min(ttraining)]/[Max(ttraining) − Min(ttraining)] Normalization t
	NN means Maximum number of iterative training d represents minimum allowable training error
	Res-Conv2 rain the ResNet-50 model by ImageNet datasets and use the first two blocks of ResNet-50 as pretrained feature extractor.
	, b Initialize parameters of Bi-LSTM module and fully connected layers
	Z = Random. Sample(X,2000) randomly select 2000 samples as training sets Z
	While N > n or L < d:
	For do: c = Res-Conv2(Z)
	h = Bi-LSTM (c) ⟶ h is the vector extracted by Bi-LSTM
	RUL = MLP(h) ⟶ The RUL value is output from fully connected layer.
	Output: the trained proposed model.