Lack of typical fault samples remains a huge challenge for intelligent fault diagnosis of
gearbox. In this paper, a novel approach named deep transfer multi-wavelet auto-encoder is …

Software defect prediction (SDP) is very helpful for optimizing the resource allocation of
software testing and improving the quality of software products. The cross-project defect …

In rotating machinery fault diagnosis, domain adaptation (DA) transfer learning-based
framework has been attracting great attentions to tackle the problem of inconsistent feature …

Transfer learning aims to apply previously learned knowledge to new unknown domains by
mining the potential relationships between data from different domains. Because of its ability …

Rolling bearings are the vital components of rotary machines. The collected data of rolling
bearing have strong noise interference, massive unlabeled samples, and different fault …

The major objective of transfer learning is to handle the learning tasks on a target domain by
utilizing the knowledge extracted from the source domain (s), when the labeled data in the …

The present research on intelligent bearing fault diagnosis assumes that the same feature
distribution is used to obtain training and testing data. However, the domain shift (distribution …

Metric learning aims at automatically learning a distance metric from data so that the precise
similarity between data instances can be faithfully reflected, and its importance has long …

The performance of the supervised learning algorithms such as k-nearest neighbor (k-NN)
depends on the labeled data. For some applications (Target Domain), obtaining such …

Compared with single-task learning, multi-tasks can obtain better classifiers by the
information provided by each task. In the process of multi-task data collection, we always …