Attention Mechanism has become very popular in the field of mechanical fault diagnosis in
recent years and has become an important technique for scholars to study and apply. The …

Deep learning methods based on vibration signals of rotating machinery have been
continuously developed in fault diagnosis. However, there are still three challenges in …

The ability of engineering systems to process multi-scale information is a crucial requirement
in the development of an intelligent fault diagnosis model. This study develops a hybrid multi …

The quality of fault recognition part is one of the key factors affecting the efficiency of
intelligent manufacturing. Many excellent achievements in deep learning (DL) have been …

With the development of industry, more and more crucial mechanical machinery generate
wildness demand of effective fault diagnosis to ensure the safe operation. Over the past few …

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 …

In recent years, the rapid development of convolutional neural networks (CNNs) has
significantly advanced the progress of intelligent fault diagnosis. Most currently-available …

Benefiting from the progress of artificial intelligence, the deep learning-based data-driven
fault diagnosis procedure has gradually become a preferred and reliable option. Although …

Rolling bearings are crucial for ensuring the safe and stable operation of electromechanical
systems. Although deep learning has been widely used in fault diagnosis of rolling bearings …

Purpose This article aims to systematically review the recent research advances in data-
driven machinery fault diagnosis based on machine learning algorithms, and provide …