Application of Machine Learning Methods with Dimension Reduction Techniques for Fault Prediction in Molding Process

Year 2020, Volume: 8 Issue: 2, 371 - 378, 26.05.2020

Fatma Demircan Keskin , İnanç Kabasakal

Abstract

Significant advances in digital technology and advanced analytical tools have had a substantial impact on the production environment and laid the foundation for Industry 4.0 and intelligent production concepts. Predictive engineering is one of the key pillars of smart manufacturing that necessitates the collection and analysis of real-time data with an anticipatory point of view through advanced analytical techniques. In the literature, machine learning-based methods have received a great deal of attention to extract valuable information from process data for fault detection. In this study, fault prediction problem was addressed in a molding process that includes successive steps by applying machine learning methods with dimension reduction techniques. The techniques of Principal Component Analysis (PCA), and Isometric Feature Mapping (Isomap) were first utilized for dimension reduction. Then, the data was analyzed for fault prediction with several machine learning techniques, namely, Support Vector Machine (SVM), Neural Network (NN), and Logistic Regression (LR). The dataset for our analysis includes sensor data captured during the molding process of a wheel rim manufacturer. Several criteria, including accuracy, area under curve (AUC), Type I, and Type II error, were employed to assess the predictive performance of the methods applied, including and the model variants reinforced with PCA and Isomap. Our study demonstrates that all predictive model variants have performed with high accuracy, ranging between 92.16% (LR) and 98.04% (PCA-NN). PCA and Isomap improved the accuracy and Type-I error measures of all models; however, no such improvement was obtained on the Type-II error rates.

Keywords

Dimension Reduction, Fault Prediction, Industry 4.0, Machine Learning, Molding Process

Application of Machine Learning Methods with Dimension Reduction Techniques for Fault Prediction in Molding Process

Abstract

Teknolojide yaşanan gelişmeler ve büyük veriyi kullanarak değer yaratmaya imkân sağlayan ileri analitik teknikler, üretim sistemleri üzerinde önemli etkiler yaratmış, Endüstri 4.0 ve akıllı imalat sistemlerinin temelini oluşturmuştur. Akıllı imalat sistemlerinin en kritik yapı taşlarından birini oluşturan kestirimci mühendislik, süreçlerden gerçek zamanlı doğru verinin toplanmasını, bu verinin ileri analitik teknikler uygulanarak öngörücü bir bakış açısıyla analiz edilmesini ve bu sayede değer yaratılmasını gerektirmektedir. Makine öğrenmesi yöntemleri, süreç verileri kullanılarak anlamlı bilgi elde etme ve hata tahmini yapmada yoğun ilgi görmektedir. Bu çalışmada, birbiri ardına gerçekleşen çok sayıda alt süreç içeren döküm sürecinde hata tahminleme problemi, makine öğrenmesi yöntemleri ve boyut indirgeme teknikleri uygulanarak ele alınmıştır. Veri setinin boyutunu indirgemek için öncelikle Temel Bileşenler Analizi (TBA) ve İzometrik Özellik Haritalama (İÖH) teknikleri uygulanmıştır. Hata tahminlemesi için, indirgenmiş veri setine, makine öğrenmesi yöntemlerinden Destek Vektör Makineleri (DVM), Yapay Sinir Ağları (YSA) ve Lojistik Regresyon (LR) uygulanmıştır. Çalışmanın veri setini, bir jant üreticisi firmanın döküm sürecinden elde edilen gerçek ölçüm değerleri oluşturmaktadır. Uygulanan makine öğrenmesi yöntemlerinin performansı ve boyut indirgeme yöntemlerinin bu yöntemlerin performansı üzerindeki etkileri, tahmin doğruluğu, eğri altındaki alan, Tip-1 hata ve Tip-2 hata ölçütleri kullanılarak değerlendirilmiştir. Tahminleyici modellerde doğruluk oranının %92,16 (LR) - 98,04 (TBA-YSA) aralığında elde edildiği görülmüştür. TBA ve İÖH tüm modellerin tahmin doğruluğu ve Tip-1 hata ölçütlerinde iyileşme sağlarken, Tip-2 hata ölçütünde aynı başarıyı yakalayamamıştır.

Keywords

Boyut İndirgeme, Hata Tahmini, Endüstri 4.0, Makine Öğrenmesi, Döküm Süreci

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