Deep Transfer Learning (DTL) signifies a novel paradigm in machine learning, merging the
superiorities of deep learning in feature representation with the merits of transfer learning in …

Deep learning-based approaches for diagnosing bearing faults have attracted considerable
attention in the last years. However, in real-world applications, these methods face …

In recent years, increasing numbers of deep learning methods for fault diagnosis of rolling
element bearings (REBS) have been proposed. However, in industry, the scarcity of …

Existing domain adaptive fault diagnosis methods hardly consider single-source-multi-target
scenarios, but the single-source-multi-target model can reduce the use of labeled data and …

Vibration signals of planetary gearboxes have complex components and time-varying
characteristics. As the unstable operation of planetary gearboxes leads to unbalanced data …

Although some traditional autoencoders and their extensions have been widely used in the
research of intelligent fault diagnosis of rotating parts, their feature extraction capabilities are …

In recent years, various deep learning techniques have been utilized for dealing with
bearing fault diagnosis. Although these methods have achieved remarkable …

The outstanding performance of current machine-learning fault diagnosis methods is mainly
attributed to the availability of a large amount of labeled training data. However, in practical …

Aiming at the problem of low diagnostic accuracy of fault diagnosis models due to changes
in actual operating conditions, a novel fault diagnosis method based on transfer learning …

In order to explore the intelligent collection of big data and network fault analysis, this paper
proposes a big data intelligent collection and network fault analysis based on artificial …