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 …

The tool is an important part of machining, and its condition determines the operational
safety of the equipment and the quality of the workpiece. Therefore, tool condition monitoring …

In order to apply the advantages of image recognition for fault diagnosis using convolutional
neural network (CNN), it is necessary to convert one-dimensional (1D) signal data into two …

Large-sized centrifugal pumps play a major role in produced water injection systems in oil
and gas production. Monitoring this equipment operation is vital to guarantee its efficiency …

In an actual engineering environment, some rotating machines are usually in normal
operation, but their time in a fault state is very short, which leads to a serious imbalance in …

Rolling bearings are a vital component of mechanical equipment. It is crucial to implement
rolling bearing fault diagnosis research to guarantee the stability of the long-term action of …

The wide application of rotating machinery has boosted the development of electricity and
aviation, however, long-term operation can lead to a variety of faults. The use of different …

We present a novel multi-modal adaptive feature extraction algorithm considering both time-
domain and frequency-domain modalities (AFETF), coupled with a Bidirectional Long Short …

With the breakthrough progress of deep learning technology in various fields, its application
in fault diagnosis and prediction of electrical systems has received more and more attention …