In modern industry, the quality of maintenance directly influences equipment's operational
uptime and efficiency. Hence, based on monitoring the condition of the machinery, predictive …

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 …

Accurate fault diagnosis is critical to ensure the safe and reliable operation of rotating
machinery. Data-driven fault diagnosis techniques based on Deep Learning (DL) have …

Intelligent fault diagnosis techniques have replaced time-consuming and unreliable human
analysis, increasing the efficiency of fault diagnosis. Deep learning models can improve the …

Abstract The Fourth Industrial Revolution incorporates the digital revolution into the physical
world, creating a new direction in a number of fields, including artificial intelligence, quantum …

The area of predictive maintenance has taken a lot of prominence in the last couple of years
due to various reasons. With new algorithms and methodologies growing across different …

With the rapid progress in the deep learning technology, it is being used for vibration-based
structural health monitoring. When the vibration is used for extracting features for system …

Intelligent fault diagnosis of bearings is an essential issue in the field of health management
and the prediction of rotating machinery systems. The traditional bearing intelligent …

Sustainability improvements in industrial production are essential for tackling climate
change and the resulting ecological crisis. In this context, resource efficiency can directly …

Data-driven rolling-bearing fault diagnosis methods are mostly based on deep-learning
models, and their multilayer nonlinear mapping capability can improve the accuracy of …