Sequential predictive maintenance and spare parts management with data mining methods: a case study in bus fleet
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Tarih
2024
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
The sustainability of enterprises in an increasingly competitive environment is proportional to their ability to use the resources efficiently. Maintenance departments are critical to ensure that resources are ready and operational. Increasing the efficiency of maintenance departments depends on reducing the number of failures, performing planned maintenance on time and sustaining availability of spare parts needed. Therefore, it is vital that businesses consider predictive maintenance and spare parts jointly. In this study, predictive maintenance and spare parts integration studies are carried out using gearbox failure data and spare parts consumption data of a bus fleet. This study aims to contribute to the reduction of failure costs by finding failure patterns and predicting the subsequent failures and spare parts to be used. The sequential pattern mining approach was used to determine failure patterns and the traditional frequent itemset mining approach was used to predict spare parts. As a result, 45 failure patterns were found. Rules with a reliability of up to 79% were obtained. In addition, spare part clusters with a support value of approximately 40% were created. With this valuable information, businesses are able to investigate root causes, take precautions against future failures, make predictions about the spare parts that will be needed, and develop joint maintenance planning and inventory management policies.
Açıklama
Anahtar Kelimeler
Sequential pattern mining; Maintenance management; Predictive maintenance; Spare parts management; Data mining
Kaynak
Journal of Supercomputing
WoS Q Değeri
N/A
Scopus Q Değeri
Q1
Cilt
80
Sayı
15
