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http://dx.doi.org/10.5762/KAIS.2019.20.10.277

Study on predicting the commercial parts discontinuance using unstructured data and artificial neural network  

Park, Yun-kyung (ILS R&d lAB, LIG Nex1 Co., Ltd)
Lee, Ik-Do (ILS R&d lAB, LIG Nex1 Co., Ltd)
Lee, Kang-Taek (ILS R&d lAB, LIG Nex1 Co., Ltd)
Kim, Du-Jeoung (ILS R&d lAB, LIG Nex1 Co., Ltd)
Publication Information
Journal of the Korea Academia-Industrial cooperation Society / v.20, no.10, 2019 , pp. 277-283 More about this Journal
Abstract
Advances in technology have allowed the development and commercialization of various parts; however this has shortened the discontinuation cycle of the components. This means that repair and logistic support of weapon system which is applied to thousands of part components and operated over the long-term is difficult, which is the one of main causes of the decrease in the availability of weapon system. To improve this problem, the United States has created a special organization for this problem, whereas in Korea, commercial tools are used to predict and manage DMSMS. However, there is rarely a method to predict life cycle of parts that are not presented DMSMS information at the commercial tools. In this study, the structured and unstructured data of parts of a commercial tool were gathered, preprocessed, and embedded using neural network algorithm. Then, a method is suggested to predict the life cycle risk (LC Risk) and year to end of life (YTEOL). In addition, to validate the prediction performance of LC Risk and YTEOL, the prediction value is compared with descriptive statistics.
Keywords
Machine Learning; Big Data; Fasttext; Neural Network; DMSMS; LC Risk; YTEOL;
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