Rotating machinery often works under time-varying speeds, and nonstationary conditions
and harsh environments make its key parts, such as rolling bearings and gears, prone to …

Most data-driven methods for fault diagnostics rely on the assumption of independently and
identically distributed data of training and testing. However, domain shift between the …

Abstract Domain generalization (DG) methods have been successfully proposed to enhance
the generalization ability of the intelligent diagnosis model. However, these methods hardly …

The domain adaptation-based intelligent diagnosis approaches have achieved promising
performance on diagnosis tasks under different working conditions. However, these …

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

With the development of technologies such as Internet of Things and big data, the realization
of fusion and cross analysis of multi-sensor signals provides the possibility for …

As an important component in rotating machines, gearbox failure will lead to costly
economic losses. Generally, incipient fault features of gearbox are weak and concealed in a …

Intelligent fault diagnosis has made significant progress in recent years. However, due to the
following two difficulties, these solutions are still difficult to implement: 1) The majority of …

Intelligent diagnosis is one of the key points of research in the field of bearing fault
diagnosis. As a representative unsupervised data expansion method, generative adversarial …

Data-driven fault diagnosis approaches have attracted considerable attention in the past few
years, and promising diagnostic performance has been achieved with sufficient monitoring …