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

OpenAlex İngilizce

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.