Intelligent fault diagnosis (IFD) refers to applications of machine learning theories to
machine fault diagnosis. This is a promising way to release the contribution from human …

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 …

Traditional artificial methods and intelligence-based methods of classifying and diagnosing
various mechanical faults with high accuracy by extracting effective features from vibration …

Due to the structure of rolling bearings and the complexity of the operating environment,
collected vibration signals tend to show strong non-stationary and time-varying …

Untimely machinery breakdown will incur significant losses, especially to the manufacturing
company as it affects the production rates. During operation, machines generate vibrations …

Prognostic and health management (PHM) can ensure that a lithium-ion battery is working
safely and reliably. The main approach of PHM evaluation of the battery is to determine the …

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

Much research has been conducted in prognostics and health management (PHM), an
emerging field in mechanical engineering that is gaining interest from both academia and …

The vibration signals measured from a rolling bearing are usually affected by the variable
operating conditions and background noise which lead to the diversity and complexity of the …

This paper presents a novel hybrid model for fault detection and classification of motor
bearing. In the proposed model, permutation entropy (PE) of the vibration signal is …