Devrim Naz AKDAŞ, Derya BIRANT, Pelin YILDIRIM TAŞER

Seyrek Öğe Seti Madenciliği Kullanılarak Otomotiv Sektöründe Dişli Üretimi Duruşlarında Anomali Tespiti

Üretimdeki duruşlar üretkenliği önemli ölçüde etkiler ve duruşların analizi başarılı ve esnek üretim için gereklidir. Üretim alanında makine duruşları ile ilgili bazı sınıflandırma ve regresyon çalışmaları yapılmış olsa da, şimdiye kadar mevcut duruş çalışmalarında seyrek öğe kümesi madenciliği (RIM) tekniği hiç uygulanmamıştır. Ayrıca, RIM kullanılarak otomotiv sektöründe dişli üretimi duruşlarında anomali tespiti henüz keşfedilmemiştir. Bu boşluğu doldurmak için, bu çalışma, iş makinelerinin dişli imalatı duruşlarındaki anomalileri tespit etmek için ilk kez RIM yönteminin uygulanmasını önermektedir. Bu çalışmada, duruşlardaki anomalileri gösteren seyrek öğe kümelerini keşfetmek için iş makinelerinin dişli imalatındaki duruşlarından oluşan gerçek dünya veri seti üzerinde Rare Pattern Growth (RP-Growth) algoritması yürütülmüştür. Deneylerde, farklı minimum destek (minsup) ve minimum seyrek destek (minraresup) eşik değerleri kullanılarak duruş verilerindeki seyrek öğe kümeleri (anomaliler) tespit edilmiştir. Elde edilen sonuçlar ayrıca öğe kümesi sayısı, yürütme süresi ve maksimum bellek kullanımı açısından da değerlendirilmiştir. Deneysel sonuçlar, Seyrek Öğe Seti Madenciliği ile Anomali Tespiti (ADRIM) isimli önerilen yaklaşımın, makine duruşlarındaki anomalieri tespit etmek için etkili bir yöntem olduğunu ve özellikle otomotiv sektöründe üretim alanında başarıyla kullanılabileceğini göstermektedir.

Anahtar Kelimeler:

Anomali tespiti, veri madenciliği, dişli üretimi, seyrek öğe seti madenciliği

Anomaly Detection for Gear Manufacturing Downtime in The Automotive Sector Using Rare Itemset Mining

Downtimes in manufacturing significantly influence productivity, and their analysis is necessary for successful and flexible production. Although some classification and regression studies have been performed on the machine downtime in the manufacturing area, the rare itemset mining (RIM) technique has never been implemented in the existing downtime studies until now. Besides, anomaly detection for gear manufacturing downtime in the automotive sector using RIM is yet to be explored. To bridge this gap, this study proposes the application of the RIM method for detecting anomalies in gear manufacturing downtime of earth moving machinery for the first time. In this study, the Rare Pattern Growth (RP-Growth) algorithm was executed on a real-world dataset consisting of downtimes in gear manufacturing of earth moving machinery to discover rare itemsets that indicate anomalies in downtimes. In the experiments, the rare itemsets (anomalies) in the downtime data were detected using different minimum support (minsup) and minimum rare support (minraresup) threshold values. The obtained results were also evaluated in terms of the number of itemsets, execution time, and maximum memory usage. The experimental results show that the proposed approach, called Anomaly Detection with Rare Itemset Mining (ADRIM), is an effective method for detecting anomalies in machine downtimes and can be successfully used in the manufacturing area, especially in the automotive sector.

Keywords:

Anomaly detection, data mining, gear manufacturing, rare itemset mining,

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