A comparative predictive maintenance application based on machine and deep learning

Year 2024, Volume: 39 Issue: 2, 1037 - 1048, 30.11.2023

Ayşenur Hatipoğlu , Yiğit Güneri , Ersen Yılmaz

https://doi.org/10.17341/gazimmfd.1221105

Abstract

In today's industry, technical equipment is evolving with an increasing complexity. More flexible maintenance strategies are of interest to provide the high reliability and sustainability of industrial equipment. Maintenance strategies are collected under three headings: preventive, corrective, and predictive maintenance. It has become imperative to monitor the data-driven industrial systems of today’s technology before potential failures occur. Predictive maintenance predicts these failures before they occur and takes the necessary action to prevent malfunctions from occurring. Predictive maintenance is a strategy which is based on both the prior and the real-time data to plan the maintenance. It is known that in industrial applications it improves overall performance, thus reducing the cost of maintenance. In this study a comparative predictive maintenance application which is based on machine and deep learning is realized. In the application classical machine learning methods and deep learning architectures are used. Logistic Regression, Naive Bayes Classifier, Decision Tree, Support Vector Machine, Random Forest, and K-Nearest Neighborhood are used as the classical machine learning methods while Long Short-Term Memory and Gated Recurrent Unit are used as the deep learning architectures. The performances of the methods are examined on the Predictive Maintenance dataset from UCI Machine Learning Repository and the results are presented comparatively in terms of metrics in detail.

Keywords

Machine learning, deep learning, predictive maintenance, classification

Makine ve derin öğrenme temelli karşılaştırmalı bir öngörücü bakım uygulaması

Abstract

Günümüz endüstrisinde teknik donanımlar artan bir karmaşıklıkla gelişmektedir. Endüstriyel donanımların yüksek güvenilirliğini ve sürdürülebilirliğini sağlamak için daha esnek bakım stratejileri ilgi çekmektedir. Bakım stratejileri; önleyici bakım, düzeltici bakım ve öngörücü bakım olmak üzere üç ana başlıkta toplanmaktadır. Günümüz teknolojisinin veri odaklı endüstriyel sistemlerini potansiyel arızalar oluşmadan önce takip etmek zorunlu hale gelmiştir. Öngörücü bakım bu arızaları oluşmadan önce tahmin eder ve oluşacak arızadan korunmak için zorunlu eylemlerin alınmasını sağlar. Öngörücü bakım, geçmiş ve gerçek zamanlı veriler üzerinde temellendirilmiş bir bakım planı stratejisidir. Endüstriyel uygulamalarda toplam başarımı iyileştirerek bakım maliyetlerini düşürdüğü bilinmektedir. Bu çalışmada, makine ve derin öğrenme temelli karşılaştırmalı bir öngörücü bakım uygulaması gerçekleştirilmiştir. Uygulamada klasik makine öğrenmesi yöntemleri ve derin öğrenme mimarileri kullanılmıştır. Klasik makine öğrenmesi yöntemi olarak Lojistik Regresyon, Naive Bayes Sınıflandırıcı, Karar Ağacı, Destek Vektör Makinesi, Rastgele Orman ve K-En Yakın Komşuluk; derin öğrenme mimarisi olarak ise Uzun Kısa Süreli Bellek ve Geçitli Tekrarlayan Birim kullanılmıştır. Yöntemlerin başarımları UCI Makine Öğrenmesi Ambarlarından alınan Öngörücü Bakım veri seti üzerinde incelenmiş ve sonuçlar karşılaştırmalı olarak ölçütler bazında detaylı bir biçimde sunulmuştur.

Keywords

Makine öğrenmesi, derin öğrenme, öngörücü bakım, sınıflandırma

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