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Machine Learning for Predictive Maintenance with Smart Maintenance Simulator
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 183 - Number 22 |
| Year of Publication: 2021 |
| Authors: Abdessalam Lmouatassime, Mohammed Bousmah |
10.5120/ijca2021921590
|
Abdessalam Lmouatassime, Mohammed Bousmah . Machine Learning for Predictive Maintenance with Smart Maintenance Simulator. International Journal of Computer Applications. 183, 22 ( Aug 2021), 35-40. DOI=10.5120/ijca2021921590
Abstract
Machine learning is a vital part of today's world. In Industry 4.0, Machine Learning approach for Predictive Maintenance continues to generate research attention, especially with the AI implementation. Indeed, the benefits of this approach such as helping determine the condition of equipment and predicting when maintenance should be performed, are extremely strategic. In this article, we propose a new Sensors Reference Model (SRM) and architecture for a Smart Maintenance Simulator (SmaSim) based on a new advanced connected sensors called Smart Sensors. This SmaSim is a low code simulator which can help researchers, engineers, and practitioners to select appropriate Smart Sensors and Machine Learning algorithms for predictive maintenance in Smart Factories.
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