INVESTIGATING THE EFFECT OF FEATURE SELECTION METHODS ON THE SUCCESS OF OVERALL EQUIPMENT EFFECTIVENESS PREDICTION

Abstract

Overall equipment effectiveness (OEE) describes production efficiency by combining availability, performance, and quality and is used to evaluate production equipment’s performance. This research’s aim is to investigate the potential of the feature selection techniques and the multiple linear regression method, which is one of the machine learning techniques, in successfully predicting the OEE of the corrugated department of a box factory. In the study, six different planned downtimes and information on seventeen different previously known concepts related to activities to be performed are used as input features. Moreover, backward elimination, forward selection, stepwise selection, correlation-based feature selection (CFS), genetic algorithm, random forest, extra trees, ridge regression, lasso regression, and elastic net feature selection methods are proposed to find the most distinctive feature subset in the dataset. As a result of the analyses performed on the data set consisting of 23 features, 1 output and 1204 working days of information, the elastic net - multiple linear regression model, which selects 19 attributes, gave the best average R2 value compared to other models developed. Occam's razor principle is taken into account since there is not a great difference between the average R2 values obtained. Among the models developed according to the principle, the stepwise selection - multiple linear regression model yielded the best R2 value among those that selected the fewest features.

Keywords

• Feature selection
• machine learning
• overall equipment effectiveness

Öznitelik Seçim Yöntemlerinin Toplam Ekipman Etkinliği Tahmin Başarısı Üzerindeki Etkisinin Araştırılması

Abstract

Toplam ekipman etkinliği (TEE); kullanılabilirliği, performansı ve kaliteyi birleştirerek üretim etkinliğini tanımlamaktadır ve üretim ekipmanının performansını değerlendirmek için kullanılmaktadır. Bu araştırmanın amacı, bir kutu fabrikasının oluklu mukavva departmanının TEE’sinin başarılı bir şekilde tahmin etmede, öznitelik seçim tekniklerinin ve makine öğrenmesi tekniklerinden biri olan çoklu doğrusal regresyon yönteminin potansiyelini araştırmaktır. Çalışmada girdi öznitelikleri olarak altı farklı planlı duruş süresi ve onyedi farklı gerçekleşecek faaliyetlere ilişkin önceden bilinen kavramlara ilişkin bilgiler kullanılmıştır. Ayrıca veri kümesinde en ayırt edici özellik alt kümesini bulmak için geriye doğru eleme, ileri doğru seçim, adımsal seçim, korelasyon tabanlı öznitelik seçim, genetik algoritma, rastgele orman, ekstra ağaç, ridge regresyon, lasso regresyon ve elastik net öznitelik seçim yöntemlerinden faydalanılmıştır. 23 öznitelikten, 1 çıktıdan ve 1204 iş günlük bilgiden oluşan veri seti üzerinde yapılan analizler neticesinde 19 adet öznitelik seçen elastik net – çoklu doğrusal regresyon modeli, geliştirilen diğer modellere kıyasla en iyi ortalama R2 değerini vermiştir. Elde edilen ortalama R2 değerleri arasında çok büyük bir fark olmaması dolayısıyla Occam’ın usturası ilkesi dikkate alınmıştır. İlkeye göre geliştirilen modellerden en az öznitelik seçenler arasında en iyi R2 değerini stepwise selection - çoklu doğrusal regresyon modeli vermiştir.

Keywords

• Öznitelik seçimi
• makine öğrenmesi
• toplam ekipman etkinliği

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