The traditional fault diagnosis models cannot achieve good fault diagnosis accuracy when a
new unseen fault class appears in the test set, but there is no training sample of this fault in …

In recent years, data-driven methods have been widely used in the field of high-voltage
circuit breakers (HVCBs) fault diagnosis. However, due to the complex mechanical structure …

Compound fault diagnosis of rotating machinery is of great significance for the operational
reliability and security of manufacturing equipment. Since the possible compound fault types …

Gearboxes, fundamental components in the domains of manufacturing, transportation, and
aerospace apparatus, are highly susceptible to impairments. The emerging technique of non …

Fault Diagnosis and Failure Prognosis on Electrical Machines, Drives, and Power
Electronics has seen rapid advancement in the last decades. Nevertheless, there is a …

The scarcity of fault samples is a common scenario in the field of fault diagnosis. In the
context of mechanical fault diagnosis, the emergence of new working conditions and fault …

Zero-sample fault diagnosis (ZSFD) achieves remarkable success and has attracted
considerable attention. However, existing methods suffer from the limitations in fault attribute …

The open environment presents a challenging issue for the online safety assessment of
dynamic systems, which means that unknown scenarios may arise unexpectedly. These …

Existing bearing fault diagnosis methods based on deep learning typically rely on a large
amount of labeled data for training. However, acquisition of a large amount of labeled target …

The absence of fault data in certain categories presents a significant challenge in data-
driven fault diagnosis, as obtaining a complete fault dataset is often unfeasible. Zero-shot …