Support matrix machine (SMM), as a typical matrix classification method, is commonly used
in the field of mechanical fault diagnosis due to its ability to fully utilize the strong correlation …

Performance degradation assessment methods for rolling bearings under vibration signal
monitoring typically involve extracting signal degradation features and inputting them …

Efficient torque estimation plays an important role for the real-time durability analysis of
vehicle components. It is desired to replace expensive torque sensors by applying soft …

In recent years, deep learning techniques have become popular for diagnosing equipment
faults. However, their real industrial application performance is hindered by challenges …

Intelligent fault diagnosis of rotating equipment is increasingly reliant on algorithms that are
driven by big data. By contrast, signal processing was once widely utilized for fault diagnosis …

The accurate health condition evaluation of the functional components in computer
numerical control (CNC) machine tools is an important prerequisite for predictive …

To solve the problem of difficulty in fault feature extraction for rolling bearings under strong
noise conditions, a K-value calculation method of variational mode decomposition (VMD) …

Basketball motion image recognition is a challenging task, and traditional methods often
face many limitations and difficulties. The purpose of this study is to explore the application …

The fault diagnosis of a gearbox is crucial to ensure its safe operation. Entropy has become
a common tool for measuring the complexity of time series. However, entropy bias may …

Dealing with classification problems requires the crucial step of feature selection (FS), which
helps to reduce data dimensions and shorten classification time. Feature selection and …