A Deep Domain-Adversarial Transfer Fault Diagnosis Method for Rolling Bearing Based on Ensemble Empirical Mode Decomposition

Table 1

A list of acronyms used in this paper.

Acronym	Full form	Acronym	Full form
EMBRNDNMD	Domain adversarial-based rolling bearing fault transfer diagnosis model	TL	Transfer learning
EMD	Empirical mode decomposition	CNN	Convolutional neural networks
EEMD	Ensemble empirical mode decomposition	ResNet	Residual network
EEMD-TFFG	Time-frequency feature graph	MMD	Maximum mean discrepancy
MBRN	Multi-branch ResNet	MK-MMD	Multi-core maximum mean difference
RNB	ResNet module	GAN	Generative adversarial network
IMF	Intrinsic mode function	DANN	Domain-adversarial neural network
IMFs	Intrinsic mode functions	GRL	Gradient reversal layer
HHT	Hilbert-Huang transform	t-SNE	t-Distributed stochastic neighbor embedding
HES	Hilbert envelope Spectrum	TCA	Transfer component analysis
SVM	Support vector machine	JDA	Joint distribution adaptation
PCA	Principal component analysis	CWRU	Case Western Reserve University
ELM	Extreme learning machine	MFS-RDS	Mechanical fault diagnosis experiment platform