Most of the current successes of deep transfer learning-based fault diagnosis require two
assumptions: 1) the health state set of source machines should overlap that of target …

As fixed-wing unmanned aerial vehicles (FW-UAVs) are used for diverse civil and scientific
missions, failure incidents are on the rise. Recent rapid developments in deep learning (DL) …

Due to the variability of working conditions and the scarcity of fault samples, the existing
diagnosis models still have a big gap under the condition of covering more practical …

Deep learning is an effective feature extraction method widely applied in fault diagnosis
fields since it can extract fault features potentially involved in multi-sensor data. But different …

Currently, few samples and the inevitable noise poses a severe test on deep learning
methods. To solve the above problems, a novel fault diagnosis network based on a refined …

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 …

Deep-learning-based intelligent diagnosis is a popular method to ensure the safe operation
of rolling bearings. However, practical diagnostic tasks are often subject to a lack of labeled …

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

Fault diagnosis has been limited due to data scarcity. Accordingly, this study focuses on fault
diagnosis representation for rolling bearing with few fault data and noisy conditions. Data …

Fault diagnosis of bearing under variable working conditions is widely required in practice,
and the combination of working conditions and fault fluctuations increases the complexity of …