In fault diagnosis, machine learning theories are gaining popularity as they proved to be an
efficient tool that not only reduces human effort but also identifies the health conditions of the …

Rotating machinery is widely used in industrial production, and its reliable operation is
crucial for ensuring production safety and efficiency. Mechanical equipment often faces the …

The analysis of electrical machine faults during start-up, and variable speed and load
conditions offers numerous advantages for fault detection and diagnosis. In this context …

Bearings are the most vulnerable component in low-voltage induction motors from a
maintenance standpoint. Vibration monitoring is the benchmark technique for identifying …

Intelligent fault detection and classification is a cornerstone of prognostic and health
management of rotating machinery (RM) research. Correctly classifying and predicting RM …

The intricate nature of compound fault diagnosis in rolling bearings during nonstationary
operations poses a challenge. To address this, a novel technique combines adaptive …

Rolling bearing fault diagnosis holds paramount significance in industrial field, as it is pivotal
for ensuring the reliability, safety, and economic viability of mechanical systems. Most of …

In recent years, various deep learning techniques have been utilized for dealing with
bearing fault diagnosis. Although these methods have achieved remarkable …

The importance of preventing failures in bearings has led to a large amount of research
being conducted to find methods for fault diagnostics and prognostics. Many of these …

In recent years, domain adaptation methods have gained widespread traction for addressing
domain-shift problems caused by distribution discrepancy across different domains in fault …