Kısıtlı kestirimci bakım için makine öğrenmesi yöntemlerinin kıyası

Yıl 2025, Cilt: 14 Sayı: 1, 183 - 191, 15.01.2025

Timur Ozkul , Ayça Topallı

https://doi.org/10.28948/ngumuh.1465282

Öz

Kestirimci bakım, duyaçların varlığı ve teçhizatların bağlanabilirliği ile son zamanlarda artan bir ilgi elde etmiştir. Yine de, özellikle eski cihazlardan geniş çapta veri elde etmek zor olabilir. Bu makale, verilerin endüstriyel bir düzenekten alınan alarm kayıtları ile sınırlı olduğu bir ortam için, geçmiş bilgileri kullanarak yakın gelecekteki bir durumu öngören akıllı bir yöntemi tanımlamaktadır. Makine öğrenmesi yöntemlerinin zaman dizisi verileri kullanarak sınıflandırma yapma işlerinde etkili olduğu kanıtlanmış olduğundan, sinir ağları, rassal orman ve aşırı eğim arttırma olarak seçilen üç yöntem, bir alarmın ve aynı makinenin kaydettiği diğer alarmların geçmiş oluşumlarından, o alarmı iki saat önceden tahmin etmek üzere eğitilmiştir. Bu üç yöntemin performansları kıyaslanmış ve hiper-parametre değerleri arasından en iyi yapılandırmayı bulmak hedeflenmiştir. Elde edilen sonuçlara göre, aşırı eğim arttırma, 500 ağaç sayısı, 128 azami derinlik ve son günden alarm oluşumları girdi penceresi ile 0.767 olan en yüksek F1 puanını vermektedir. Bu çalışma, makinelerin işlemesi ve bakımı hakkında potansiyel olarak önemli anlayışlar sağlayan ve dikkate değer masraf azaltma imkânları sunan alarm öngörüleri için en iyi makine öğrenmesi yöntemini belirlemeyi hedefleyen kıyaslamalı bir araştırmadan oluşmaktadır.

Anahtar Kelimeler

Makine Öğrenmesi, Sinir Ağları, Kestirimci Bakım, Rassal Orman, Aşırı Eğim Arttırma

Comparison of machine learning methods for limited predictive maintenance

Öz

Predictive maintenance has gained increasing attention recently with the availability of sensors and connectivity of equipment. Yet, it would be difficult to obtain a wide range of data, especially with legacy devices. This paper describes an intelligent method for predicting a near future condition using the past information for an environment in which data are limited to the alarm logs from industrial machinery. Since machine learning methods are proven to be efficient in classification tasks using time series data, three of them are selected to predict an alarm two hours in advance using the past occurrences. These methods are neural networks, random forests, and extreme gradient boosting. The performances of these three methods are compared, and it is aimed to find the optimal configuration among hyper-parameter values. According to the obtained results, extreme gradient boosting gives the highest F1-score of 0.767 with number of trees equal to 500, maximum depth of 128, and an input window of alarm occurrences from the last day. This work consists of a comparative study aiming to identify the best machine learning method for alarm predictions, which potentially provides important insights into the operation and maintenance of machinery, bringing the possibility of considerable cost reductions.

Anahtar Kelimeler

Machine Learning, Neural Networks, Predictive Maintenance, Random Forest, Extreme Gradient Boosting

Kaynakça

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