This paper presents an empirical study of feature extraction methods for the application of
low-speed slew bearing condition monitoring. The aim of the study is to find the proper …

The rolling element bearing is one of the most critical components that determine the
machinery health and its remaining lifetime in modern production machinery. Robust …

This paper presents a new method for bearing fault diagnosis using the fusion of two primary
sensors: an accelerometer and a load cell. A novel condition-based monitoring (CBM) …

Prognostic and Health Management (PHM) systems are some of the main protagonists of
the Industry 4.0 revolution. Efficiently detecting whether an industrial component has …

Abstract System health monitoring is a set of activities performed on a system to maintain it
in operable condition. Monitoring may be limited to the observation of current system states …

A bearing fault diagnosis method has been proposed based on multi-scale entropy (MSE)
and adaptive neuro-fuzzy inference system (ANFIS), in order to tackle the nonlinearity …

This paper presents a new approach to machine health monitoring based on the
Approximate Entropy (ApEn), which is a statistical measure that quantifies the regularity of a …

A K-means clustering approach is proposed for the automated diagnosis of defective rolling
element bearings. Since K-means clustering is an unsupervised learning procedure, the …

A new rolling bearing fault diagnosis method based on multi-scale fuzzy entropy (MFE),
Laplacian Score (LS) and variable predictive model-based class discrimination (VPMCD) is …

The development of non-linear dynamic theory brought a new method for recognising and
predicting the complex non-linear dynamic behaviour. Fractal dimension can quantitatively …