The fault detection and diagnosis (FDD) along with condition monitoring (CM) and of rotating
machinery (RM) have critical importance for early diagnosis to prevent severe damage of …

Nowadays, condition-based maintenance (CBM) and fault diagnosis (FD) of rotating
machinery (RM) has a vital role in the modern industrial world. However, the remaining …

To overcome current challenges in variational mode decomposition (VMD) and its variants
for the fault diagnosis of rotating machines, the decomposing characteristics of two sub …

Intelligent bearing fault diagnosis based on deep learning is one of the hotspots in
mechanical equipment monitoring applications. However, traditional deep learning-based …

Bearing weak fault feature extraction under time-varying speed conditions is a challenging
task. The classic time-frequency analysis (TFA) based ridge detection algorithms cannot …

Bearings are a critical component of rotating machines; when they fail, critical equipment
becomes unavailable, damage may occur beyond the bearing itself, and safety concerns …

Recently proposed variational signal decomposition methods like adaptive chirp mode
decomposition (ACMD) and generalized dispersive mode decomposition (GDMD) have …

In the modern manufacturing industry, mechanical equipment plays a crucial role.
Equipment working in harsh environments for a long time is more likely to break down …

The measured vibration signals of mechanical equipment with defects are generally non-
stationary. Time–frequency analysis is an effective tool in processing non-stationary signals …

Despite enormous research, material, and manufacturing advancements in bearings, these
continue to fail in critical aerospace applications. Material and processing aspects of …