Rolling element bearings play a crucial role in the functioning of rotating machinery.
Recently, the use of diagnostics and prognostics methodologies assisted by artificial …

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 …

The objective of this paper is to provide a review of recent advances in automatic vibration-
and audio-based fault diagnosis in machinery using condition monitoring strategies. It …

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) …

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 …

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 …

In this paper a novel method for de-noising nonstationary vibration signal and diagnosing
diesel engine faults is presented. The method is based on the adaptive wavelet threshold …

Intelligent fault diagnosis of bogie is fundamental for the reliability, stability, and security of a
high-speed train (HST). However, thorny issues, including complicated structure of bogie …

A novel method based on wavelet leaders multifractal features for rolling element bearing
fault diagnosis is proposed. The multifractal features, combined with scaling exponents …