In recent studies, Transformer collaborated with convolution neural network (CNN) have
made certain progress in the field of intelligent fault diagnosis by leveraging their respective …

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 …

Fault diagnosis plays a critical role in ensuring the reliability and safety of industrial systems.
Despite the success of semi-supervised learning in fault diagnosis, challenges persist in …

Fault diagnosis is subject to the challenge of implementing model learning in the presence
of small samples and imbalanced data (ie, variable operating conditions), which is a …

Accurate fault detection is extremely important to ensure stable gearbox operation. Data-
driven schemes using cyclic spectral have received significant attention due to their robust …

As a facilitator of smart upgrading, digital twin (DT) is emerging as a driving force in
prognostics and health management (PHM). Faults can lead to degradation or malfunction …

Although deep learning techniques have been widely used in machinery fault diagnosis, the
key issue of feature learning under multiple non-ideal conditions, ie, strong noise …

Monitoring the health of drilling pumps and diagnosing faults is crucial for the smooth
operation of oil drilling activities. However, existing deep learning algorithms struggle with …

The long-tailed distribution of monitoring data poses challenges for deep learning-based
fault diagnosis (FD). Recent efforts utilizing supervised contrastive learning (SCL) and …

Deep learning methods can learn effective representations from the data, simplifying the
fault diagnosis process and improving accuracy. However, the lack of data presents a …