Data-driven drift detection and diagnosis framework for predictive maintenance of heterogeneous production processes: Application to a multiple tapping process

Authors

Bertrand Rose (ORCID: https://orcid.org/0000-0002-7833-7290)
Alexandre Voisin (ORCID: https://orcid.org/0000-0002-4637-6826)
L. J. Chaves
Julien Chapelin (ORCID: https://orcid.org/0000-0002-2258-4890)
Benoît Iung (ORCID: https://orcid.org/0000-0001-5560-4096)
Lionel Steck
Olivier Jotz
Mathieu Lauer

Institutions

Laboratoire des Sciences de l'Ingénieur, de l'Informatique et de l'Imagerie (FR)
Centre de Recherche en Automatique de Nancy (FR)
Université de Lorraine (FR)

Publication Details

Journal: Engineering Applications of Artificial Intelligence
Published: 2024-10-31
DOI: https://doi.org/10.1016/j.engappai.2024.109552
Citations: 12
Primary Topic: Fault Detection and Control Systems
Type: article
Field-Weighted Citation Impact: 3.94

Funders

Université de Strasbourg

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article

Data-driven drift detection and diagnosis framework for predictive maintenance of heterogeneous production processes: Application to a multiple tapping process

Bertrand Rose, Alexandre Voisin, L. J. Chaves, Julien Chapelin et al.

Oct 31, 2024

12 citations

Engineering Applications of Artificial Intelligence

Fault Detection and Control Systems

3.94

article

Data-driven drift detection and diagnosis framework for predictive maintenance of heterogeneous production processes: Application to a multiple tapping process

Bertrand Rose, Alexandre Voisin, L. J. Chaves, Julien Chapelin, Benoît Iung, Lionel Steck, Olivier Jotz, Mathieu Lauer

October 31, 2024article en

12 citations

Abstract

No abstract available for this paper.

Engineering Applications of Artificial IntelligenceVol. 139

Laboratoire des Sciences de l'Ingénieur, de l'Informatique et de l'Imagerie (FR), Centre de Recherche en Automatique de Nancy (FR), Université de Lorraine (FR)

Université de Strasbourg

Openalex Percentile: Top 6%

Fault Detection and Control Systems

3.94

DOI Open Access PDF OpenAlex

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