Ensemble learning combines several individual models to obtain better generalization
performance. Currently, deep learning architectures are showing better performance …

In this survey paper, we systematically summarize existing literature on bearing fault
diagnostics with deep learning (DL) algorithms. While conventional machine learning (ML) …

Unsupervised domain adaptation (UDA)-based methods have made great progress in
bearing fault diagnosis under variable working conditions. However, most existing UDA …

With significant advantages in feature learning, the deep learning-based compound fault
(CF) diagnosis method has brought many successful applications for industrial equipment; …

This article develops new deep learning methods, namely, deep residual shrinkage
networks, to improve the feature learning ability from highly noised vibration signals and …

Rotating machinery intelligent diagnosis based on deep learning (DL) has gone through
tremendous progress, which can help reduce costly breakdowns. However, different …

Unsupervised domain adaptation (UDA)-based methods have made great progress in
mechanical fault diagnosis under variable working conditions. In UDA, three types of …

Accurate prediction of remaining useful life (RUL) has been a critical and challenging
problem in the field of prognostics and health management (PHM), which aims to make …

With the rapid development of smart manufacturing, data-driven fault diagnosis has attracted
increasing attentions. As one of the most popular methods applied in fault diagnosis, deep …

Accurate fault diagnosis is critical to ensure the safe and reliable operation of rotating
machinery. Data-driven fault diagnosis techniques based on Deep Learning (DL) have …