The theoretical developments of data-driven fault diagnosis methods have yielded fruitful
achievements and significantly benefited industry practices. However, most methods are …

Intelligent fault diagnosis, detection, and prognostics (DDP) for complex equipment
prognostics and health management (PHM) have achieved remarkable breakthroughs …

Cross-domain machinery fault diagnosis aims to transfer enriched diagnosis knowledge
from a labeled source domain to a new unlabeled target domain. Most existing methods …

Abstract Electro-Hydrostatic Actuator (EHA) is a critical electro-hydraulic actuator system
widely used in aerospace equipment. To ensure its normal operation, the intelligent fault …

Data-driven fault diagnosis techniques are frequently applied to ensure the reliability and
safety of industrial systems. However, as a common challenge, the class-imbalance problem …

The current data-level and algorithm-level based imbalanced fault diagnosis methods have
respective limitations such as uneven data generation quality and excessive reliance on …

Rolling bearings are critical for maintaining the stability, reliability, and safety of mechanical
systems. However, diagnosing faults in rolling bearings objectively can be challenging due …

Intelligent fault diagnosis methods based on deep learning have attracted significant
attention in recent years. However, it still faces many challenges, including complex and …

Most current research on multi-source domain adaptation in bearing fault diagnosis focuses
on training domain-agnostic networks whose parameters are static. However, it is …

Deep learning-based fault diagnosis methods have already attained remarkable
achievements in this field. However, rolling bearing frequently operates under variable …