Due to the limited fault samples, the accuracy of the bearing fault diagnosis model is
challenged. Therefore, this article proposes a bearing fault diagnosis method based on …

In practical engineering, the working conditions of gearbox are complex and variable. In
varying working conditions, the performance of intelligent fault diagnosis model is degraded …

The burgeoning field of the Industrial Internet of Things (IIoT) necessitates advanced fault
diagnosis methods capable of navigating the dual challenges of high predictive accuracy …

Vibration signals, serving as critical sources of information for monitoring the status of
rotating machinery, demand effective extraction and rational utilization of its features to …

Due to the complex environment and limited hardware resources in the industrial practice
diagnosis tasks, deploying deep learning-based models with large parameters is …

Lempel–Ziv complexity indicator (LZCI), as one of the complexity indicators, is effectively
used for identifying the bearing fault severity due to its own advantages. However, it has …

Accurate prediction of bearing failures is crucial for reducing maintenance costs and
enhancing production efficiency in rotating machinery. However, the variable speed …

Current approaches to defect detection and segmentation make essential use of machine
learning methods. To develop lightweight models is one of key tasks for many defect …

Feature selection is a crucial step in fault diagnosis. When rolling bearings are susceptible
to compound faults, causal relationships are hidden within the signal features. Complex …

To solve the problems of insufficient feature extraction of the current methods under small
sample conditions and information loss in the process of signal transformation from different …