The traditional deep learning-based bearing fault diagnosis approaches assume that the
training and test data follow the same distribution. This assumption, however, is not always …

Transfer learning can effectively solve the target task identification problem with the
prerequisite of sharing all user data and target data, and has become one of the most …

Deep Transfer Learning (DTL) signifies a novel paradigm in machine learning, merging the
superiorities of deep learning in feature representation with the merits of transfer learning in …

Recent years have seen the rapid development and marvelous achievement of deep
learning-based fault diagnosis (FD) methods which assume that training data and testing …

Recent years have witnessed the successful development of domain adaptation methods to
tackle cross-domain fault diagnosis problems. However, these methods require the target …

As an important component of the rotating machinery, rolling bearings usually work under
the condition of variable speed and load, and vibration signals in the same health state are …

With the advances in artificial intelligence, there is a growing expectation of more automatic
and intelligent prognostics and health management (PHM) systems for the real-time …

In recent years, many deep transfer learning methods have been widely used in bearing
fault diagnosis under varying working conditions to solve the problem of data distribution …

For industrial scenarios with changing operating conditions, the vibration data of different
operating conditions often have different data distributions. In this article, to make the deep …

Transfer learning is an excellent approach to deal with the problem that the target domain
label can not be adequately obtained when rolling bearing cross-condition fault detection. A …