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 …

Most data-driven methods for fault diagnostics rely on the assumption of independently and
identically distributed data of training and testing. However, domain shift between the …

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 …

Discrepancy representation metric completely determines the transfer diagnosis
performance of deep domain adaptation methods. Maximum mean discrepancy (MMD) …

Rolling bearing fault detection is critical for improving production efficiency and lowering
accident rates in complicated mechanical systems, as well as huge monitoring data, posing …

The research on intelligent fault diagnosis has yielded remarkable achievements based on
artificial intelligence-related technologies. In engineering scenarios, machines usually work …

With the emergence of machine learning methods, data-driven fault diagnosis has gained
significant attention in recent years. However, traditional data-driven diagnosis approaches …

Battery degradation, caused by multiple coupled degradation mechanisms, severely affects
the safety and sustainability of a battery management system (BMS). The battery state of …

A smart factory is a highly digitized and connected production facility that relies on smart
manufacturing. Additionally, artificial intelligence is the core technology of smart factories …

This paper highlights the importance of maintenance techniques in the coming industrial
revolution, reviews the evolution of maintenance techniques, and presents a comprehensive …