Limited condition monitoring data are recorded with label information in practice, which
make the fault identification task more challenging. A semi-supervised learning (SSL) …

Deep learning has been widely used nowadays to achieve an automated fault diagnosis of
rolling bearings. However, most of deep learning based bearing fault diagnosis methods are …

Recently, intelligent fault diagnosis has made great achievements, which has aroused
growing interests in the field of bearing fault diagnosis due to its strong feature learning …

Early fault diagnosis is a hotspot and difficulty in the research of mechanical fault diagnosis.
An early fault diagnosis method based on the orthogonal neighborhood preserving …

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

Fault diagnosis is essentially a kind of pattern recognition. The measured signal samples
usually distribute on nonlinear low-dimensional manifolds embedded in the high …

Intelligent data-driven fault diagnosis methods for rolling element bearings have been
widely developed in the recent years. In real industries, the collected machinery signals are …

Fault diagnosis of rolling bearing is essential to guarantee production efficiency and avoid
catastrophic accidents. Domain adaptation is emerging as a critical technology for the …

Intelligent fault diagnosis of rolling element bearings has made some achievements based
on the availability of massive labeled data. However, the available data from bearings used …

The shortage of labeled data is a major obstacle to the practical application of advanced
fault diagnosis technologies, and the large amount of unlabeled data may be the key to …