Considering the nonstationary characteristics of the vibration signal of aircraft engine rolling
bearings and the insufficient ability of convolutional neural network (CNN) to extract …

In recent years, deep learning methods, especially convolutional neural network (CNN),
have received extensive attentions and applications in fault diagnosis. However, recent …

In recent years, deep learning has shown great vitality in the field of intelligent fault
diagnosis. However, most diagnostic models are not yet capable enough to capture the rich …

It is always challenging and meaningful to further enhance the feature extraction capability
of the convolutional neural network (CNN) and understand the internal working principle of …

Convolutional neural networks (CNNs) have demonstrated promising effectiveness in
vibration-based fault diagnosis. However, the faulty characteristics are usually distributed on …

Rolling bearing fault signals are non-smooth, non-linear, and susceptible to background
noise interference. A feature layer fusion model combining multi-sensor signals and parallel …

In recent years, deep learning technique has been used in mechanical intelligent fault
diagnosis and it has achieved much success. Among the deep learning models …

Accurate and real-time fault diagnosis (FD) and working conditions identification (WCI) are
the key to ensuring the safe operation of mechanical systems. We observe that there is a …

In the past year, various deep learning-based fault diagnosis methods have been designed
to guarantee the stable, safe, and efficient operation of electromechanical systems. To …

In recent years, deep learning has achieved great success in bearing fault diagnosis due to
its robust feature learning capabilities. However, in the actual industry, the diagnostic …