With the emergence of machine learning methods, data-driven fault diagnosis has gained
significant attention in recent years. However, traditional data-driven diagnosis approaches …

The world we see is ever-changing and it always changes with people, things, and the
environment. Domain is referred to as the state of the world at a certain moment. A research …

Deep learning has produced state-of-the-art results for a variety of tasks. While such
approaches for supervised learning have performed well, they assume that training and …

In this paper, we propose a new unsupervised domain adaptation approach called
Collaborative and Adversarial Network (CAN) through domain-collaborative and domain …

This paper intends to provide an overview on recent development of knowledge transfer for
rotary machine fault diagnosis (RMFD) by using different transfer learning techniques. After …

Recently, considerable effort has been devoted to deep domain adaptation in computer
vision and machine learning communities. However, most of existing work only concentrates …

Deep transfer learning (DTL), which incorporates new ideas from deep neural networks into
transfer learning (TL), has achieved excellent success in computer vision, text classification …

The accuracies for many pattern recognition tasks have increased rapidly year by year,
achieving or even outperforming human performance. From the perspective of accuracy …

In this paper, we make two contributions to unsupervised domain adaptation (UDA) using
the convolutional neural network (CNN). First, our approach transfers knowledge in all the …

Unsupervised domain adaptation deals with a task from an unlabeled target domain by
leveraging the knowledge gained from labeled source domain (s). The fuzzy system is …