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 …

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 real industries, there often exist application scenarios where the target domain holds fault
categories never observed in the source domain, which is an open-set domain adaptation …

Blind deconvolution has been proved to be an effective method for fault detection since it
can recover periodic impulses from mixed fault signals convoluted by noise and periodic …

Deep learning (DL) based methods have swept the field of mechanical fault diagnosis,
because of the powerful ability of feature representation. However, many of existing DL …

Deep neural networks (DNN) have remarkably progressed in applications involving large
and complex datasets but have been criticized as a black-box. This downside has recently …

Abstract Advanced models of Artificial Intelligence enable systems of IoT to work with great
flexibility to the needs of users. In this article we present our developed IoT system for driving …

With advanced measurement technologies and signal analytics algorithms developed
rapidly, the past decades have witnessed large amount of successful breakthroughs and …

The use of artificial neural network models to enrich the analytical and predictive capabilities
of decision support systems in manufacturing has increased. The growing complexity and …

Deep learning (DL)-based intelligent fault diagnosis methods have greatly promoted the
development of the field of fault diagnosis due to their powerful feature extraction ability for …