E3S Web Conf.
Volume 351, 202210th International Conference on Innovation, Modern Applied Science & Environmental Studies (ICIES’2022)
|Number of page(s)||5|
|Published online||24 May 2022|
Development process to bearing fault diagnostic and prognostic for the predictive maintenance era
Laboratory of Industrial Engineering and Seismic Engineering, ENSA National School of Applied Sciences Oujda, Mohammed Premier University, Oujda, Morocco
* Corresponding author: email@example.com
Today, the manufacturing industry seeks to improve competitiveness by converging on new technologies to ensure a new engine of growth, moreover, systems based on IoT and artificial intelligence are increasingly used in this convergence. This new industry must meet the challenges of productivity and competitiveness to interconnect the physical and digital world in which machines, information systems, and products communicate permanently, all to reduce consumers and maintain productivity gains and optimize them in terms of energy consumed reduced breakdowns... This article presents an original and innovative contribution. A new model has been proposed that summarizes an approach based on machine learning, intending to perform predictive maintenance based on artificial neural networks, considering the values acquired by sensors in real-time, it allows us a fast and very low implementation of predictive maintenance, particularly important for companies. The model is validated in real situations. The results show a very high level of accuracy.
© The Authors, published by EDP Sciences, 2022
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.