The theoretical developments of data-driven fault diagnosis methods have yielded fruitful
achievements and significantly benefited industry practices. However, most methods are …

In practical mechanical fault detection and diagnosis, it is difficult and expensive to collect
enough large-scale supervised data to train deep networks. Transfer learning can reuse the …

Most RUL prediction methods can only extract single-scale features, ignoring significant
details at other scales and layers. These methods are all constructed using one type of …

Intelligent fault diagnosis based on deep learning has yielded remarkable progress for its
strong feature representation capability in recent years. Nevertheless, in engineering …

Playing a vital role in keeping gearbox working reliably and safely, smart fault diagnosis
(FD) technology has attracted much attention in recent years. However, in practical industrial …

社会发展的同时带来大量数据的产生, 不平衡成为众多数据集的显著特点, 如何使不平衡数据集
得到更好的分类效果成为了机器学习的研究热点. 基于此, 对目前存在的不平衡数据集分类方法 …

Limited fault data restrict deep learning methods in solving fault diagnosis problems in
rotating machinery. Using limited fault data to generate massive data with similar …

Abstract Domain adaptation (DA) have achieved phased results in fault transfer diagnosis.
However, there is still no unified framework that not only minimize geometrical shift but also …

Transfer learning-based fault diagnosis methods, especially unsupervised domain
adaptation (UDA), have demonstrated significant potential in addressing insufficiently …

Bearings are very important components in mechanical equipment, and detecting bearing
failures helps ensure healthy operation of mechanical equipment and can prevent …