Signal Processing is, arguably, the fundamental enabling technology for vibration-based
Structural Health Monitoring (SHM), which includes damage detection and more advanced …

Rolling element bearings are broadly applied in various industrial machines, such as
railway axles, gearboxes, electric motors, and turbines. Bearing fault diagnosis is important …

重大装备制造业是国民经济的支柱, 也是关系到国家安全的战略性产业, 而重大机械装备的运行
安全一直是备受关注的焦点. 机械设备由于工作环境恶劣, 工况复杂, 其关键部件容易受损 …

Variational mode decomposition is a completely non-recursive decomposition model, where
all the modes are extracted concurrently. However, the model requires a preset mode …

Traditional feature extraction methods are used to extract the features of signal to construct
the fault feature matrix, which exists the complex structure, higher correlation, and …

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

Various existed time-series decomposition methods, including wavelet transform, ensemble
empirical mode decomposition (EEMD), local characteristic-scale decomposition (LCD) …

Empirical wavelet transform is good at distinguishing components containing different
frequency information in complex signals. Due to the higher complexity of the Fourier …

Extracting the failure related information from vibration signals is a very important aspect of
vibration-based fault detection for rolling bearing Multiscale entropy and its improvement …

This paper presents a novel vibration signal fusion algorithm using improved empirical
wavelet transform and variance contribution rate to fuse three-channel vibration signals for …