Abstract Multi-Source Information Fusion (MSIF), as a comprehensive interdisciplinary field
based on modern information technology, has gained significant research value and …

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 …

In recent studies, Transformer collaborated with convolution neural network (CNN) have
made certain progress in the field of intelligent fault diagnosis by leveraging their respective …

Fault diagnosis and prognosis (FDP) tries to recognize and locate the faults from the
captured sensory data, and also predict their failures in advance, which can greatly help to …

Fault diagnosis of the bearing is vital for the safe and reliable operation of rotating machines
in the manufacturing industry. Convolutional neural networks (CNNs) have been popular in …

The fault diagnosis methods based on convolutional neural network (CNN) have achieved
many excellent results. However, owing to the deployment cost, numerous CNNs with large …

Aiming at the problems of low accuracy and robustness of traditional deep learning fault
diagnosis methods, a novel attention-based multi-feature parallel fusion model …

With the rapid development of pattern recognition represented by deep learning, the
massive excellent bearing fault diagnosis methods have emerged. However, the majority of …

In engineering practice, small-sample fault diagnosis of mechanical equipment towards
heavy noise interference poses great challenges for the existing Transformer based …

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