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 …

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 …

Data scarcity is a major challenge when training deep learning (DL) models. DL demands a
large amount of data to achieve exceptional performance. Unfortunately, many applications …

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 …

Despite a few recent meta-learning studies have facilitated few-shot cross-domain fault
diagnosis of bearing, they are limited to homogenous signal analysis and have challenges …

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 …

Intelligent data-driven machinery fault diagnosis methods have been successfully and
popularly developed in the past years. While promising diagnostic performance has been …

With the popularization of the intelligent manufacturing, much attention has been paid in
such intelligent computing methods as deep learning ones for machinery fault diagnosis …

In the past years, the practical cross-domain machinery fault diagnosis problems have been
attracting growing attention, where the training and testing data are collected from different …

The data-driven methods in machinery fault diagnosis have become increasingly popular in
the past two decades. However, the wide applications of this scheme are generally …