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.

The available labeled samples are scarce when high-end machineries work in different operating-load conditions. There are often new faults present when conducting transfer fault diagnosis, leading to performance degradation. How to accurately identify them under dynamic load-variable conditions is a more challenging issue. Therefore, the filter-match-interact transfer framework (FMI-TF) is proposed, which consists of three interactive networks. Open samples filtering: learn the known–unknown samples classification hyperplane by designing the progressive filtering discriminator, achieving target samples distraction and progressive outliers filtering. Weighted auxiliary matching: align domain distributions and tighten known–unknown samples boundary through the entropy-modified weighted matching mechanism, the auxiliary distracting classifier, and the high-confidence negative probabilities of unknown samples. Interactive refinement rectification: mine and cultivate information interaction and coupling within two networks by improving the differentiated interactive updating module, and achieving positive network transfer. The FMI-TF has been validated on different mechanical testbeds.