Real-time health condition monitoring of bearings plays a significant role in the functionality
of the rotary machinery. Multi-channel sensor fusion can be more robust for identifying …

In a real industrial scenario, domain shift frequently occurred due to working loads variation,
operation speeds variation, and environmental noise interference, severely degrading …

Deep learning-based methods have been developed and widely used for fault diagnosis,
which rely on the sufficient data. However, fault data are extremely limited in some real-case …

As a powerful tool to solve nonlinear problems, artificial neural network method (ANN) gets a
wide range of applications in data regression. However, the overfitting often occurs during …

The imbalance of fault modes prevails in industrial equipment monitoring. Many methods
were presented for imbalanced fault diagnosis only by resampling labeled fault dataset …

Dictionary learning has made enormous achievements for its powerful feature
representation capabilities. For the bearing fault diagnosis, the lack of failure samples is …

Stacked denoising autoencoder is one of the most classic models of deep learning.
However, there are two problems in the traditional stacked denoising autoencoder:(1) the …

In this article, fault diagnosis is of great significance for system health maintenance. For real
applications, diagnosis accuracy suffers from unbalanced data patterns, where normal data …

In recent years, deep learning based diagnostic approaches have become more attractive.
However, most of these methods are supervised diagnostic approaches. Developing a …

It is very important and necessary to diagnose bearing faults timely, quickly, and accurately
in practical applications, because the operation status of the bearings is directly related to …