Knowledge and data dual-driven (KDDD) represents a novel paradigm that leverages the
strengths of data-driven methods in feature representation and knowledge transfer, while …

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 …

Data-driven fault diagnosis has been a hot topic in recent years with the development of
machine learning techniques. However, the prerequisite that the training data and the test …

Fault diagnosis is an important topic both in practice and research. There is intense pressure
on industrial systems to continue reducing unscheduled downtime, performance …

Intelligent fault diagnosis is a vital role in smart manufacturing. And deep-learning-based
fault diagnosis has become a hot topic due to its strong feature extraction ability. However …

Fault diagnosis and prognosis (FDP) tries to recognize and locate the faults from the
captured sensory data, and also predict their failures in advance, which can greatly help to …

Industrial Internet of Things (IIoT) systems connect a plethora of smart devices, such as
sensors, actuators, and controllers, to enable efficient industrial productions in manners …

In the fourth industrial revolution, data-driven intelligent fault diagnosis for industrial
purposes serves a crucial role. In contemporary times, although deep learning is a popular …

Despite rapid advances in machine learning based fault diagnosis, their identical
distribution assumption of the training (source domain) and testing data (target domain) is …

With the advent of industry 4.0, machine learning (ML) methods have mainly been applied to
design condition-based maintenance strategies to improve the detection of failure …