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- Title
KESTİRİMCİ BAKIM PLANLAMA İÇİN MAKİNE ÖĞRENMESİ TEMELLİ BİR KARAR DESTEK SİSTEMİ VE BİR UYGULAMA.
- Authors
SOYLU, Banu; YİĞİTER, Hatice; SARIKAYA, Venüs; SANDIKÇI, Zinnet; UTKU, Asena
- Abstract
Purpose: In order to prevent breakdowns in production systems, predictive maintenance planning using Industry 4.0 infrastructure has been the focus of companies. In this study, a predictive maintenance decision support system integrated with internet-of-things (IoT) was developed and a pilot study was carried out on a machine to minimize the breakdowns and production downtime. Methodology: Temperature, humidity, and sound sensors have been used in order to provide continuous monitoring of the system. The data obtained with these sensors is transferred to a database via a network using IoT. In order to predict the system state (“breakdown may occur”, “good”) from this data, the machine learning techniques (Support Vector Machine and Decision Tree) are used. Findings: The proposed decision support system is able to make self-maintenance decision. Thus, it would be possible to minimize 1419 min. downtime of the machine that the pilot study was performed on. Originality: The effect of production sequence on system breakdowns has been investigated with sequential pattern mining algorithms. A maintenance decision support system including the integration of IoT, machine learning, predictive maintenance, sequential pattern mining and dynamic scheduling has been developed.
- Publication
Verimlilik Dergisi, 2022, p48
- ISSN
1013-1388
- Publication type
Article
- DOI
10.51551/verimlilik.988104