Banu SOYLU, Hatice YİĞİTER, Venüs SARIKAYA, Zinnet SANDIKÇI, Asena UTKU

KESTİRİMCİ BAKIM PLANLAMA İÇİN MAKİNE ÖĞRENMESİ TEMELLİ BİR KARAR DESTEK SİSTEMİ VE BİR UYGULAMA

Amaç: Üretim sistemlerinde meydana gelen arızaları önlemek için Endüstri 4.0 altyapısını kullanan kestirimci bakım planlama işletmelerin gündemine girmiştir. Bu çalışmada, bir sistemde meydana gelen arızaların ve üretim duruşlarının en küçüklenmesi için nesnelerin interneti (IoT) ve makine öğrenmesi tabanlı bakım karar destek sistemi oluşturulmuş ve bir makine üzerinde pilot çalışma yapılmıştır. Yöntem: Bu çalışmada, sistemin sürekli izlenebilirliğini sağlamak için sıcaklık, nem ve ses sensörleri kullanılmıştır. Bu sensörlerle alınan veriler IoT kullanılarak veri tabanına bir ağ aracılığı ile aktarılmıştır. Aktarılan bu verilerden sistemin durumunu (“arıza olabilir”, “sağlam”) tahmin etmek için makine öğrenmesi teknikleri (Destek Vektör Makinesi ve Karar Ağacı) kullanılmıştır. Geçmiş arıza kayıtları ve geçmiş üretim planları birleştirilerek makineye gelen ürün sırasının arızaya etkisi sıralı örüntü madenciliği yöntemleri ile araştırılmıştır. Bulgular: Geliştirilen karar destek sistemi, bakım kararı verebilmektedir. Böylece pilot çalışma yapılan makinede gerçekleşmiş olan 1419 dk. beklenmeyen duruşların en küçüklenmesi sağlanacaktır. Özgünlük: Yenilikçi bir yön olarak; sisteme giren ürün sırasının da arızaya etkisinin olabileceği sıralı örüntü madenciliği yöntemleriyle incelenmiştir. IoT, makine öğrenmesi, kestirimci bakım, sıralı örüntü madenciliği ve dinamik çizelgelemenin entegrasyonunu içeren bir bakım karar destek sistemi oluşturulmuştur.

Anahtar Kelimeler:

Nesnelerin İnterneti (IoT), Makine Öğrenmesi Teknikleri, Kestirimci Bakım, Sıralı Örüntü Madenciliği, Dinamik Çizelgeleme

A MACHINE LEARNING-BASED DECISION SUPPORT SYSTEM FOR PREDICTIVE MAINTENANCE PLANNING AND AN APPLICATION

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. Historical breakdown records and production plan information are merged in order to find out the effect of production schedule on machine breakdowns. 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.

Keywords:

Internet-of-Things (IoT), Machine Learning Techniques, Predictive Maintenance, Sequential Pattern Mining, Dynamic Scheduling,

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