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 Machine health management has become the focus of equipment monitoring
upgrading with the advance of digital twin (DT). The DT model is able to generate system …

The mainstream approach to addressing the issues of insufficient historical data and high
annotation costs in the domain of rotating machinery is to build transfer learning models …

In practical engineering scenarios, the operating speed of mechanical equipment is intricate
and variable. However, much of the existing research on intelligent fault diagnosis is …

Blind deconvolution (BD) has been demonstrated to be an efficacious approach for
extracting bearing fault-specific features from vibration signals under strong background …

In recent years, deep learning has been extensively applied to bearing fault diagnosis with
remarkable achievements. However, in real industrial scenarios, the primary challenge in …

To date, various meta-learning methods have been explored to face the data-scarcity
problem in fault diagnosis. Almost without exception, these methods work on the premise …

Unanticipated background noises often convolute fault information in the gearboxes'
vibration response. The Blind Deconvolution strategy has been extensively applied for fault …

This paper proposes a novel approach to predicting the useful life of rotating machinery and
making fault diagnoses using an optimal blind deconvolution and hybrid invertible neural …

In recent years, fault diagnosis based on fusion data has become a research hotspot, but
most of the existing fusion methods are based on single-mode signals, which not only has …