A New Framework Based on Supervised Joint Distribution Adaptation for Bearing Fault Diagnosis across Diverse Working Conditions

Table 7

Comparison of experimental results between DFCD-IDAMN and relevant methods from other literatures.

Abbreviation of methods	DA method	Literature	Experimental data	Cross-domain tasks	Maximum mean accuracy (%)
DFCD-IDAMN	IDAMN	This article	Bearing data from CWRU and our own test-bed	12 tasks under 4 working conditions	99.57
MTSDE [56]	MMD	A novel cross-domain intelligent fault diagnosis method based on entropy features and transfer learning	Bearing from PHM2009, CWRU, and MFPT	6 diagnosis tasks under 2 working conditions	97.10
BARTL [3]	BDA	Balanced adaptation regularization based transfer learning for unsupervised cross-domain fault diagnosis	Bearing data from Jiangnan university and Politecnico di Torino	6 diagnosis tasks under 2 working speeds	98.73
FT-IDJ [57]	JDA	An intelligent fault diagnosis method for rolling bearings based on feature transfer with improved DenseNet and joint distribution adaptation	Bearing data from CWRU	12 diagnosis tasks under 4 working speeds	98.50
TCA-based [58]	TCA	Transfer learning based data feature transfer for fault diagnosis	Bearing data from CWRU	6 diagnosis tasks under 2 working speeds	91.40
AMPD [26]	GFK	A new transferable bearing fault diagnosis method with adaptive manifold probability distribution under different working conditions	Bearing data from own test rig	12 diagnosis tasks under 4 working speeds	98.85
JGSA-FTFE [59]	JGSA	Time frequency feature analysis of rolling bearing fault based on deep transfer learning	Bearing data from CWRU and own test-bed	2 diagnosis tasks under 2 working conditions	95.55

The bold values highlight that the experimental results are desirable.