Rolling element bearings are essential components of a wide variety of industrial machinery
and the leading cause of equipment failure. The prediction of Remaining Useful Life (RUL) …

This paper proposes a novel tangent hyperbolic fuzzy entropy measure-based method for
determining the highly (most) sensitive frequency band to easily identify defective …

Effective condition monitoring and fault diagnosis of rolling bearings, integral components of
rotating machinery, are crucial for ensuring equipment reliability. However, existing …

Oil and gas extraction is vital for meeting the energy needs of a growing global population.
Artificial lift (AL) systems play a crucial role in oilfields, especially when reservoir pressure is …

The dynamic response of bearings under vibration excitation environment is an important
theoretical basis for studying the occurrence and extension analysis of abnormal bearing …

The case dependency of vibration-based Structural Damage Detection (SDD) models on
their training structure (data) has always been a setback in their application to new …

Vibration analysis has been extensively exploited for bearing fault diagnosis. However,
signal acquisition is quite expensive since external hardware is required. Moreover, for …

Featured Application When one is uncertain whether the potential fault is unbalancing or
shaft bow based on frequency analysis, the proposed approach is applicable to issues …

Detecting bearing defects accurately and efficiently is critical for industrial safety and
efficiency. This paper introduces Bearing-DETR, a deep learning model optimised using the …

This paper is presented to diagnose and root cause analysis of the bearing failure. For this
purpose, a vibration investigation was carried out on a motor with a 2300 kW power, 1489 …