Recently, the progress of intelligent fault diagnosis shows deep learning-based methods
with large data have achieved great success. Nevertheless, in engineering practice, limited …

At present, the success of most intelligent fault diagnosis methods is heavily dependent on
large datasets of artificial simulation faults (ASF), which have not been widely used in …

Data‐driven fault diagnosis approaches have been widely adopted due to their persuasive
performance. However, data are always insufficient to develop effective fault diagnosis …

Deep learning (DL) based diagnosis models have to be trained by large quantities of
monitoring data of machines. However, in real-case scenarios, machines operate under the …

Deep learning-based fault diagnosis methods have made tremendous progress in recent
years; however, most of these methods are coarse grained and data demanding that cannot …

The data-driven diagnosis methods based on conventional machine-learning techniques
have been widely developed in recent years. However, the assumption of conventional …

Few-shot data collected from hoisting system suffer from inadequate information in the
practical industries, which reduces the diagnostic accuracy of the data-driven-based fault …

Intelligent fault diagnosis methods are significant to mitigate the dependency on expert
knowledge and the cost. For the limited faulty data and variational working conditions of real …

It has always been an issue of significance to diagnose compound faults of machines.
Existing intelligent diagnosis methods have to be trained by sufficient data of each …

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