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Automatic classification of rotating machinery defects using Machine Learning (ML) algorithms

Zoungrana Wend-Benedo, Chehri Abdellah et Zimmermann Alfred. (2020). Automatic classification of rotating machinery defects using Machine Learning (ML) algorithms. Dans Human centred intelligent systems. (p. 193-203). Smart Innovation, Systems and Technologies. Singapore : Springer.

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URL officielle: http://dx.doi.org/doi:10.1007/978-981-15-5784-2_16

Résumé

Electric machines and motors have been the subject of enormous development. New concepts in design and control allow expanding their applications in different fields. The vast amount of data have been collected almost in any domain of interest. They can be static; that is to say, they represent real-world processes at a fixed point of time. Vibration analysis and vibration monitoring, including how to detect and monitor anomalies in vibration data are widely used techniques for predictive maintenance in high-speed rotating machines. However, accurately identifying the presence of a bearing fault can be challenging in practice, especially when the failure is still at its incipient stage, and the signal-to-noise ratio of the monitored signal is small. The main objective of this work is to design a system that will analyze the vibration signals of a rotating machine, based on recorded data from sensors, in the time/frequency domain. As a consequence of such substantial interest, there has been a dramatic increase of interest in applying Machine Learning (ML) algorithms to this task. An ML system will be used to classify and detect abnormal behavior and recognize the different levels of machine operation modes (normal, degraded, and faulty). The proposed solution can be deployed as predictive maintenance for Industry 4.0.

Type de document:Chapitre de livre
Date:30 Mai 2020
Lieu de publication:Singapore
Sujets:Sciences naturelles et génie > Génie
Sciences naturelles et génie > Génie > Génie informatique et génie logiciel
Sciences naturelles et génie > Sciences appliquées
Département, module, service et unité de recherche:Départements et modules > Département des sciences appliquées > Module d'ingénierie
Éditeurs:Zimmermann, Alferd
Howlett, Robert J.
Jain, Lakhmi C.
Liens connexes:
Mots-clés:classification, Machine Learning (ML) algorithms, SVM, k-Nearest Neighbor, decision trees, artificial intelligence, Naive Bayes, predictive maintenance, Industry 4.0, algorithmes d'apprentissage automatique (ML), arbres de décision, intelligence artificielle, maintenance prédictive, Industrie 4.0, Proceedings
Déposé le:13 mai 2021 21:09
Dernière modification:13 mai 2021 21:09
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