Monitoring vibrations in rotating machinery allows effective diagnostics, as abnormal
functioning states are related to specific patterns that can be extracted from vibration signals …

A significant development in condition monitoring techniques has been observed over the
years. The scope of condition monitoring has been shifted from defect identification to its …

In the current study, a new structural damage detection algorithm is presented using the
modified Hilbert–Huang transform and support vector machine. The modified Hilbert–Huang …

Bearing pitting, one of the common faults in mechanical systems, is a research hotspot in
both academia and industry. Traditional fault diagnosis methods for bearings are based on …

In this communication, an attempt has been made to develop an algorithm for automatic
prediction of the size of the bearing defect during operation of a machine. Features for the …

Taking aim at the nonstationary nonlinearity of the rolling bearing vibration signal, a rolling
bearing fault diagnosis method based on the entropy fusion feature of complementary …

A fault diagnosis approach based on empirical mode decomposition (EMD), Hilbert
envelope spectrum and Kurtosis criterion is proposed, by considering the nonlinear and non …

Classical Hilbert-Huang transform (HHT) is commonly used in bearing vibration signal
analysis and fault feature extraction, which consists of two parts: Empirical Mode …

With the increasing complexity of mechanical equipment and diversification of deep learning
models, vibration signals collected from such equipment are susceptible to noise …

As a prerequisite for rotating machinery to operate effectively, rolling element bearings play
an essential role. The focus of condition monitoring has initially been on defect identification …