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WITS 2020 pp 145-156 | Cite as

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

Conference paper
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Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 745)

Abstract

In the industrial environment, production systems are increasingly complex and cannot be free from disturbances and failures. Indeed, the following study is considered as a point of change in the service domain to effectively track disturbances and failures, by allowing the transition from old maintenance to smart maintenance. However, the following document represents a sort of passage between the old and the new maintenance by treating the operation of the bearings in the rotating mechanical systems, the study consists in studying the modes of failures of the bearings. A prediction model is developed based on neural networks.

Keywords

Predictive maintenance Bearings Prognosis Artificial intelligence 

References

  1. 1.
    Jeffali F, El Kihel B, Nougaoui A, Delaunois F (2015) Monitoring and diagnostic misalignment of asynchronous machines by infrared thermography. J Mater Environ Sci 6:1192–1199Google Scholar
  2. 2.
    Mukesh PS, Bulsara A (2016) Energy loss due to unbalance in rotor–shaft system. J Eng Des Technol 14(2):277–362Google Scholar
  3. 3.
    Saleem MA, Diwakar G, Satyanarayana MRS (2012) Detection of unbalance in rotating machines using shaft deflection measurement during its operation. J Mech Civ Eng (iosr-jmce) 3(3):8–20Google Scholar
  4. 4.
    Harrouche F (2019) Etude Comparative de Deux Méthodes D’optimisation d’un Système de Classification des Défauts Mécaniques par la Logique Floue. Thèse de doctorat, Université Ferhat Abbas Sétif AlgérieGoogle Scholar
  5. 5.
    Mattioli J, Robic P-O, Reydellet T (2018) L’intelligence artificielle au service de la maintenance prévisionnelle, 4ème conférence sur les Applications Pratiques de l’Intelligence Artificielle APIA2018, Nancy, FranceGoogle Scholar
  6. 6.
    Bouzidi Z (2018) Pronostic des systèmes industriels basé sur l’intelligence artificielle Maintenance prédictive. Thèse de doctorat, Faculté des Sciences Exactes et des Sciences de la Nature et de la Vie Département d’informatique, Université Mohamed Khider—BISKRAGoogle Scholar
  7. 7.
    Ayegba PO, Abdulkadir M, Hernandez-Perez V, Lowndes IS, Azzopardi BJ (2017) Applications of artificial neural network (ANN) method for performance prediction of the effect of a vertical 90° bend on an air–silicone oil flow. J Taiwan Inst Chem Eng 74:59–64.  https://doi.org/10.1016/j.jtice.2017.02.005CrossRefGoogle Scholar
  8. 8.
    Bouallegue K (2017) A new class of neural networks and its applications. Neurocomputing 249:28–47.  https://doi.org/10.1016/j.neucom.2017.03.006CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2022

Authors and Affiliations

  1. 1.Laboratory of Engineering, Industrial Management and Innovation, Faculty of Science and Technology of SettatUniversity Hassan 1erCasablancaMorocco
  2. 2.Laboratory of Industrial Engineering and Seismic EngineeringNational School of Applied Sciences ENSA-Oujda, Mohammed Premier UniversityOujdaMorocco
  3. 3.Laboratory of Products Energy and Sustainable DevelopmentEST, University Sidi Mohamed Ben AbdellahFezMorocco

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