Abstract Deep Transfer Learning (DTL) is a new paradigm of machine learning, which can
not only leverage the advantages of Deep Learning (DL) in feature representation, but also …

Autoencoder is an unsupervised learning model, which can automatically learn data
features from a large number of samples and can act as a dimensionality reduction method …

Most of the existing research on unsupervised cross-domain intelligent fault diagnosis is
based on single-source domain adaptation, which fails to simultaneously utilize various …

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 …

Deep learning has been the answer to many machine learning problems during the past two
decades. However, it comes with two significant constraints: dependency on extensive …

Many domain adaptation methods have been presented to deal with the distribution
alignment and knowledge transfer between the target domain and the source domain …

Manufacturing organizations need to use different kinds of techniques and tools in order to
fulfill their foundation goals. In this aspect, using machine learning (ML) and data mining …

The existing deep transfer learning-based intelligent fault diagnosis studies for machinery
mainly consider steady speed scenarios, and there exists a problem of low diagnosis …

Intelligent fault diagnosis (IFD) refers to applications of machine learning theories to
machine fault diagnosis. This is a promising way to release the contribution from human …

The existing fault diagnosis methods of rotating machinery constructed with both shallow
learning and deep learning models are mostly based on vibration analysis under steady …