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 …

Deep learning-based intelligent diagnostic methods have been widely used in aerospace,
rail transportation, automotive, rail vehicles, and other fields. However, deep neural …

Intelligent fault diagnosis (IFD) methods allow effective feature extraction of mechanical big
data and perform well in fault diagnosis tasks. Numerous domain adaptation (DA)-based IFD …

Transfer learning has attracted great attention in intelligent fault diagnosis of bearings under
different working conditions. However, existing studies have the following limitation.(1) The …

The vibration signal of a bearing is closely related to its fault. The quality of the features
extracted from the signal has a great impact on the accuracy of fault diagnosis. In this paper …

Unsupervised cross-domain fault diagnosis of bearings has practical significance; however,
the existing studies still face some problems. For example, transfer diagnosis scenarios are …

Big-data fault diagnosis methods based on deep learning (DL) have been widely studied in
recent years. However, the number of labeled bearing fault samples is limited in industrial …

In recent years, deep learning-based fault diagnosis methods have drawn lots of attention.
However, for most cases, the success of machine learning-based models relies on the …

Deep neural networks for bearing fault diagnosis have become the focus of research in
recent years with its excellent feature extraction capability. However, the problem of …

Deep learning-based bearing fault diagnosis methods have been developed to learn fault
knowledge from massive data. Owing to the deficiency of fault samples and the variability of …