Real-time bearing fault diagnosis plays a vital role in maintaining the safety and reliability of
sophisticated industrial systems. However, the scarcity of labeled data in fault diagnosis, due …

This study presents an efficient vibration-based fault detection method for rotating machines
utilising the poly-coherent composite spectrum (pCCS) and machine learning techniques …

Rolling bearings are essential in rotating machinery, but their operation under diverse
conditions like load, speed, and temperature can lead to complex faults. While efforts have …

The operational reliability of rolling element bearings is crucial for the consistency of
mechanical systems in various industries. Early detection of defects significantly enhances …

Machine learning (ML) and artificial intelligence (AI) have emerged as the most advanced
technologies today for solving issues as well as assessing and forecasting occurrences. The …

Synchronous analysis is one of the most effective and practical techniques in rotating
machinery diagnostics, especially in cases with variable speed operations. A modern …

Recently, deep learning has received widespread attention in the field of bearing fault
diagnosis due to its powerful feature learning capability. However, when the actual working …

Bearing defect detection plays a crucial role in the intelligent production of chemical
transmission equipment, where timely identification and handling of defective bearings are …

Purpose The purpose of this study is to solve the problem of how to effectively separate and
extract different state features under the influence of coupling of different state features and …

This research aims to determine the initial diagnosis of damage to a 3-phase induction
motor. Ball bearings are one of the important components in this induction motor which play …