Techniques for Apply Predictive Maintenance and Remaining Useful Life: A Systematic Mapping Study

Year 2021, , 497 - 511, 30.06.2021

Begüm Ay Türe Akhan Akbulut Abdül Halim Zaim

https://doi.org/10.35193/bseufbd.900214

Cited By: 6

Abstract

With prognostic activities, it is possible to predict the remaining useful life (RUL) of industrial systems with high accuracy by following the current health status of devices. In this study, we have collected 199 articles on predictive maintenance and remaining useful life. The aim of our systematic mapping study is to determine which techniques and methods are used in the areas of predictive maintenance and remaining useful life. Another thing we aim is to give an idea about the main subject to the researchers who will work in this field. We created our article repository by searching databases such as IEEE and Science Direct with certain criteria and classified the articles we obtained. By applying the necessary inclusion and exclusion criteria in the article pool we collected, the most appropriate articles were determined and our study was carried out through these articles. When we focused on the results, it was learned that the SupportVector Machine algorithm is the most preferred predictive maintenance method. Most studies aimed at evaluating the performance and calculating the accuracy of the results used the Root Mean Square Error algorithm. In our study, every method and algorithm included in the articles are discussed. The articles were examined together with the goals and questions we determined, and results were obtained. The obtained results are explained and shown graphically in the article. According to the results, it is seen that the topics of predictive maintenance and remaining useful lifetime provide functionality and financial gain to the environment they are used in. Our study was concluded by light on many questions about the application of predictive maintenance.

Keywords

Predictive Maintenance, Remaining Useful Life, Machine Learning, Deep Learning, Root Mean Square Error

Kestirimci Bakım ve Kalan Yararlı Ömür Uygulama için Teknikler: Sistematik Haritalama Çalışması

Abstract

Prognostik faaliyetler ile endüstriyel sistemlerin kalan yararlıömrünü (RUL), mevcut sağlık durumlarının takip ederek yüksek doğrulukta tahmin edilmesi mümkündür. Bu çalışmadakestirimci bakım ve kalan faydalı ömür hakkında 199 makale topladık. Sistematik haritalamaçalışmamızın amacı, kestirimci bakım ve kalan faydalı ömür alanlarında hangi teknik ve yöntemlerin kullanıldığını belirlemektir. Amaçladığımız bir diğer konu da bu alanda çalışacak araştırmacılara ana konu hakkında fikir vermektir. IEEE ve Science Direct gibi veritabanları belirli kriterler ile aranarak makale havuzu oluşturuldu ve elde edilen makaleler sınıflandırıldı. Toplanılan makale havuzunda gerekli dahil etme ve hariç tutma kriterleri uygulanarak en uygun makaleler belirlendi ve çalışmamız bu makaleler üzerinden gerçekleştirildi. Sonuçlara odaklandığımızda Destek Vektör Makinesi algoritmasının en çok tercih edilen kestirimci bakım yöntemi olduğu öğrenildi. Performansı değerlendirmeyi ve sonuçların doğruluğunu hesaplamayı amaçlayan çoğu çalışmada Kök Ortalama Kare Hatası algoritması kullanılmıştır. Çalışmamızda makalelerde yer alan her yöntem ve algoritma tartışılmıştır. Makaleler, belirlediğimiz amaç ve sorularla birlikte incelenerek sonuçlar elde edilmiştir. Elde edilen sonuçlar makalede açıklanmış ve grafik olarak gösterilmiştir. Elde edilen sonuçlara göre, kestirimci bakım ve kalan faydalı ömür konularının, kullanıldıkları ortama işlevsellik ve finansal kazanç sağladığı görülmüştür. Çalışmamız, kestirimci bakım uygulaması ile ilgili birçok soruyu aydınlatarak sonuçlandırılmıştır.

Keywords

Kestirimci Bakım, Kalan Yararlı Ömür, Makine Öğrenmesi, Derin Öğrenme, Kök Ortalama Kare Hata

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