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Product failure prediction with missing data

Authors
Kang, SeokhoKim, EunjiShim, JaewoongChang, WonsangCho, Sungzoon
Issue Date
2018
Publisher
TAYLOR & FRANCIS LTD
Keywords
data mining; predictive modelling; failure prediction; production data; missing value
Citation
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, v.56, no.14, pp 4849 - 4859
Pages
11
Journal Title
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
Volume
56
Number
14
Start Page
4849
End Page
4859
URI
https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/63956
DOI
10.1080/00207543.2017.1407883
ISSN
0020-7543
1366-588X
Abstract
In production data, missing values commonly appear for several reasons including changes in measurement and inspection items, sampling inspections, and unexpected process events. When applied to product failure prediction, the incompleteness of data should be properly addressed to avoid performance degradation in prediction models. Well-known approaches for missing data treatment, such as elimination and imputation, would not perform well under usual scenarios in production data, including high missing rate, systematic missing and class imbalance. To address these limitations, here we present a method for predictive modelling with missing data by considering the characteristics of production data. It builds multiple prediction models on different complete data subsets derived from the original data-set, each of which has different coverage of instances and input variables. These models are selectively used to make predictions for new instances with missing values. We demonstrate the effectiveness of the proposed method through a case study using actual data-sets from a home appliance manufacturer.
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Kim, Eunji
경영경제대학 (경영학부(서울))
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