Segmentation of machines with RFM analysis: an evaluation based on maintenance and failure records

Basit Öğe Kaydı
dc.contributor.authorCanlı, Hikmet
dc.contributor.authorVarıcı, Sena
dc.date.accessioned2025-09-01T07:01:55Z
dc.date.available2025-09-01T07:01:55Z
dc.date.issued2025
dc.departmentFakülteler, Mühendislik Fakültesi, Yazılım Mühendisliği Bölümü
dc.description7th International Conference on Intelligent and Fuzzy Systems, INFUS 2025 --29-31 July 2025 -- Istanbul
dc.description.abstractThis study aims to evaluate the segmentation of machines based on maintenance and failure records using RFM (Recency, Frequency, Monetary) analysis. By analyzing the maintenance and failure data of the machines, the failure history of each machine has been examined. The main objective is to assess machine segmentation using parameters such as Failure Frequency, Total Failure Duration, and Last Failure Time. These parameters are integrated into the RFM analysis to understand the operational health of the machines and to determine the necessary strategies for preventing failures. The results facilitate the segmentation of machines and the development of tailored maintenance and improvement strategies for each segment. This study offers a data-driven approach to more accurately predict machine failure trends, optimize maintenance processes, improve operational efficiency, and reduce costs.
dc.identifier.doi10.1007/978-3-031-98304-7_41
dc.identifier.endpage368
dc.identifier.isbn9783031983030
dc.identifier.issn2367-3370
dc.identifier.scopus2-s2.0-105013077380
dc.identifier.scopusqualityQ4
dc.identifier.startpage361
dc.identifier.urihttps://doi.org/10.1007/978-3-031-98304-7_41
dc.identifier.urihttps://hdl.handle.net/11501/2337
dc.identifier.volume1531 LNNS
dc.identifier.wosWOS:001587127900041
dc.identifier.wosqualityN/A
dc.indekslendigikaynakScopus
dc.indekslendigikaynakWeb of Science
dc.institutionauthorCanlı, Hikmet
dc.institutionauthorVarıcı, Sena
dc.institutionauthorid0000-0003-3394-7113
dc.institutionauthorid0009-0006-0749-4623
dc.language.isoen
dc.publisherSpringer International Publishing AG
dc.relation.ispartof7th International Conference on Intelligent and Fuzzy Systems, INFUS 2025
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectClassification
dc.subjectData Analysis
dc.subjectPredictive Maintenance
dc.subjectRFM Analysis
dc.titleSegmentation of machines with RFM analysis: an evaluation based on maintenance and failure records
dc.typeConference Object

Dosyalar

Lisans paketi
Listeleniyor 1 - 1 / 1
Kapalı Erişim
İsim:
license.txt
Boyut:
1.17 KB
Biçim:
Item-specific license agreed to upon submission
Açıklama:
İndir

Koleksiyon

Mühendislik Fakültesi Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu
WoS İndeksli Yayınlar Koleksiyonu