In severe data imbalance scenarios, fault samples are generally scarce, challenging the
health management of industrial machinery significantly. Generative adversarial network …

Federated Learning is a promising tool for fault diagnosis of critical components for electrical
driving systems. However, the performance of existing method is limited by negative …

Cross domain fault diagnosis based on deep learning is of great significance for improving
the reliability and safety of mechanical equipment. Generally, it assumes that the label sets …

Traditional deep learning fails to identify new faults when the number of faulty samples is
limited. Existing meta-learning studies on cross-domain small-sample fault diagnosis do not …

Intelligent fault diagnosis models have de-monstrated superior performance in industrial
prognostics health management scenarios. However, these models may struggle to …

Data-driven fault diagnosis methods often require abundant labeled examples for each fault
type. On the contrary, real-world data is often unlabeled and consists of mostly healthy …

Accurate bearing fault diagnosis is of great significance of the safety and reliability of rotary
mechanical system. In practice, the sample proportion between faulty data and healthy data …

The abnormal detection of rotating machinery under small sample size conditions is of prime
importance in the field of fault diagnosis. In this work, we proposed an unsupervised …

Deep transfer learning provides an advanced analytical tool for intelligent fault diagnosis to
learn shared fault knowledge in industrial scenarios whereby datasets are collected from …

The maturation of sensor network technologies has promoted the emergence of the
Industrial Internet of Things, which has been collecting an increasing volume of monitoring …