A Hybrid Deep Learning Prediction Method of Remaining Useful Life for Rolling Bearings Using Multiscale Stacking Deep Residual Shrinkage Network

Algorithm 1

Stacking-based algorithm for prediction network process construction.

	Input: Training set
	Output: Predicted value after integration H
	Process:
	Step 1: Data preprocessing
	for i = 1 to m do
	abs (mean())
	end for
	= do CUSUM on
	for i = 1 to m do
	if i <  set  = 0
	else set  =
	end for
	normalization()
	Step 2: Training base-learner
	for t = 1 to T do
	learn based on D
	end for
	Step 3: Feature aggregation
	for i = 1 to m do
	, where
	end for
	Step 4: Training meta-learner
	learn H based on
	Step 5: Smoothing the curve
	smooth H
	return H