Endüstriyel Makinelerin Arıza Durumlarına Göre Segmentasyonu: K-means ve Fuzzy C-means Algoritmaları ile RFM Analizi

Öz

Bu çalışma makinelerin segmentasyonunu, bakım ve arıza kayıtlarına dayalı olarak RFM analizi ile değerlendirdikten sonra K-means ve Fuzzy C-means kümeleme algoritmaları kullanarak değerlendirmeyi amaçlamaktadır. Her bir makinenin arıza geçmişi makinelerin bakım ve arıza verileri analiz edilerek incelenmiştir. Makinelerin segmentasyonunu değerlendirmek amacıyla Arıza Frekansı, Toplam Arıza Süresi ve Son Arıza zamanı gibi parametreler kullanılmıştır. Bu parametreler, müdahale edilmesi gereken makinelerin belirlenmesini ve makinelerin operasyonel sağlık durumlarını anlaşılmasını sağlamıştır. Makine verileri üzerinde RFM analizi uygulandıktan sonra K-means ve Fuzzy C-means algoritmaları kullanılarak kümeleme yapılmıştır. Bu çalışma, makinelerin bakım süreçlerini optimize etmek, arıza eğilimlerini daha doğru tahmin etmek, operasyonel verimliliği artırmak ve maliyetleri düşürmek için veri odaklı bir yaklaşım sunmaktadır. Çalışma sonuçları David-Bouldin Index, Dunn Index, Calinski-Harabasz Index gibi metrikler kullanılarak kıyaslanmış ve en iyi kümelemeyi yapan algoritma seçilmiştir. Sonuçlar, makinelerin segmentlere ayrılmasını ve her segment için özel bakım ve iyileştirme stratejilerinin geliştirilmesini sağlamaktadır.

Anahtar Kelimeler

• RFM Analizi
• K-means
• Fuzzy C-means
• Kestirimci Bakım

Segmentation of Industrial Machines Based on Fault Conditions: RFM Analysis with K-means and Fuzzy C-means Algorithms

Abstract

This study aims to evaluate machine segmentation based on maintenance and failure records using RFM analysis, followed by clustering with K-means and Fuzzy C-means algorithms. The failure history of each machine was analyzed based on maintenance and failure data. Parameters such as Failure Frequency, Total Failure Time, and Last Failure Time were used to assess the segmentation of the machines. These parameters helped identify machines that require intervention and better understand their operational health status. After applying RFM analysis to the machine data, clustering was performed using K-means and Fuzzy C-means algorithms. This study presents a data-driven approach to optimize machine maintenance processes, more accurately predict failure trends, increase operational efficiency, and reduce costs. The results were compared using metrics such as the David-Bouldin Index, Dunn Index, and Calinski-Harabasz Index, and the best clustering algorithm was selected. The findings facilitate the segmentation of machines and the development of specific maintenance and improvement strategies for each segment.

Keywords

• RFM Analysis
• K-means
• Fuzzy C-means
• Predictive Maintenance

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