The research on intelligent fault diagnosis has yielded remarkable achievements based on
artificial intelligence-related technologies. In engineering scenarios, machines usually work …

The traditional fault diagnosis models cannot achieve good fault diagnosis accuracy when a
new unseen fault class appears in the test set, but there is no training sample of this fault in …

Transfer learning-based fault diagnosis methods have revealed prominent application
prospects. However, most of these methods can achieve efficient knowledge transfer across …

The advances of intelligent fault diagnosis in recent years show that deep learning has
strong capability of automatic feature extraction and accurate identification for fault signals …

Data-driven fault diagnosis has been a hot topic in recent years with the development of
machine learning techniques. However, the prerequisite that the training data and the test …

The most existing deep neural networks (DNN)-based methods for fault diagnosis only focus
on prediction accuracy without considering the limitation of labeled sample size. In practical …

In the engineering practice, lacking of data especially labeled data typically hinders the wide
application of deep learning in mechanical fault diagnosis. However, collecting and labeling …

Compound fault diagnosis plays a critical role in lowering the maintenance time and cost of
industrial robots. With the advance of deep learning and industrial big data, a compound …

Intelligent fault diagnosis based on deep neural networks and big data has been an
attractive field and shows great prospects for applications. However, applications in practice …

Fault diagnosis of chemical process data becomes one of the most important directions in
research and practice. Conventional fault diagnosis and classification methods first extract …