The application belongs to the technical field of fault diagnosis, and particularly relates to a twin data multi-
modal fusion transfer diagnosis method for gear fault, which comprises the following steps: obtaining three mode components of
Hilbert envelope spectrum, autocorrelation
time domain waveform and autocorrelation envelope spectrum of gear measured and simulated signals, and respectively constructing three source domain subsets and three target domain subsets; performing JMMD mapping alignment on the source domain subsets and the target domain subsets of the same mode; training three independent
DBSCAN classifiers by using the JMMD mapping features of the three source domain subsets respectively, and performing pseudo-
label labeling on the JMMD mapping features of the target domain same mode components; updating the target domain sample labels by using a Sugeno fuzzy integral
decision fusion method; repeating the JMMD mapping and the Sugeno fuzzy integral
decision fusion until the maximum iteration number is reached, and obtaining a
fault recognition result; and the application is supported by
simulation data, and can realize accurate recognition of gear fault without the guidance of measured
label data, and has a good application prospect in the field of gear fault diagnosis.