Universal Fault Diagnosis With Unknown Label Space of Machineries Based on Evaluate-Match-Constrain Transfer Network

The high-end machineries often suffer from a scarcity of labeled fault samples under alternating load conditions. In addition, class transitions often hinder diagnosis performance. To address these challenges, the evaluate-match-constrain transfer network (EMC-TNet) is proposed, which includes three components: stochastic confident estimation (SCE), enhanced weighted matching (EWM), and interactive refined constraint (IRC). The SCE enhances high-confidence estimations by designing stochastic auxiliary classifiers, achieving the division of known–unknown samples under swarm intelligence. The EWM suppresses private classes of source samples and achieves the effective supervision of target samples by generating enhanced weighted labels through the sample classifier. In addition, it improves discrimination between known–unknown samples by assigning weights via auxiliary classifiers. The IRC refines the classifier discrimination threshold adopting the Shannon entropy weighting strategy and the consistency decision mechanism from dual classifiers, achieving high-precision recognition of known–unknown samples. The effectiveness of the EMC-TNet has been verified on different mechanical testbeds.