Rotating machinery plays a critical role in many significant fields. However, the
unpredictable machinery faults may lead to the severe damage and losses. Hence, it is of …

Rolling bearing fault diagnosis is a meaningful yet challengeable task. To improve the
performance of rolling bearing fault diagnosis, this paper proposes an enhanced selective …

Due to the complex and rugged working environment of real machinery equipment, the
resulting fault information is easily submerged by severe noise interference. Additionally …

Variational mode decomposition provides a feasible method for non-stationary signal
analysis, but the method is still not adaptive, which greatly limits the wide application of the …

Fault diagnosis methods based on dimensionless indicators have long been studied for
rotating machinery. However, traditional dimensionless indicators frequently suffer a low …

Vibration-based feature extraction of multiple transient fault signals is a challenge in the field
of rotating machinery fault diagnosis. Variational mode decomposition (VMD) has great …

A novel fault diagnosis procedure based on improved symplectic geometry mode
decomposition (SGMD) and optimized SVM is presented. In the proposed procedure, a …

Evaluating and predicting bearing performance degradation is essential to the reliability and
safety of mechanical equipment systems. However, due to the complex working conditions …

Mechanical fault diagnosis under varying speed conditions has gradually become an
important issue in rotating machinery monitoring, especially the research on compound fault …

Electromyographic signals contaminated with noise during the acquisition process affect the
results of follow-up applications such as disease diagnosis, motion recognition, gesture …