Spare Part Demand Prediction Based on Context-Aware Matrix Factorization View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2015-11-13

AUTHORS

Jianwei Ding , Yingbo Liu , Yuan Cao , Li Zhang , Jianmin Wang

ABSTRACT

Maintenance spare part is used to replace and update the damaged and old components in the equipment. Forecasting spare part demand is notoriously difficult, as demand is typically intermittent and lumpy. Meanwhile, with the development of the sensor and internet technology, numerous condition monitoring systems are used to monitor the working condition of equipment, generating a large variety of monitor data at runtime. In this paper, we propose a Spare Part Demand (SPD) model based on a context-aware matrix factorization approach. The SPD mode incorporates historical spare part demands, the correlation between spare part demands and working places, and the correlation between spare part demands and monitor data. We evaluate our method based on extensive experiments using historical spare demands of one important component from more than 10000 concrete pump trucks and monitor data generated by part of these pump concrete pump trucks over a period of 9 months. The results demonstrate the advantages of our method over the previous studies, validating the contribution of our method. More... »

PAGES

304-315

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-25255-1_25

DOI

http://dx.doi.org/10.1007/978-3-319-25255-1_25

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1044046473


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