Artificial Intelligence Based on the Neurons Networks at the Service Predictive Bearing

Elkihel, Ali; Derouiche, Imane; Elkihel, Yosra; Bakdid, Amar; Gziri, Hassan

doi:10.1007/978-981-33-6893-4_14

Cited by 4 publications

(1 citation statement)

References 3 publications

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“…It promises failure anticipation, quality improvements, and availability of automated production lines [4,5]. Predictive maintenance not only predicts possible failure but also identifies problems in complex machines and recognizes parts for repair [6,7]. To address this problem, businesses must generally digitize their operations by applying different technology levers that should support decentralized choices via system connection, digital transformation, and instantaneous communication.…”

Section: Introductionmentioning

confidence: 99%

Development process to bearing fault diagnostic and prognostic for the predictive maintenance era

Bouyahrouzi

Kihel

2022

E3S Web Conf.

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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.

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