- Volume 19 Issue 8
DOI QR Code
Identify the Failure Mode of Weapon System (or equipment) using Machine Learning
Machine Learning을 이용한 무기 체계(or 구성품) 고장 유형 식별
- Park, Yun-Kyung (ILS R&D Lab, LIG Nex1 Co., Ltd.) ;
- Lee, Hye-Won (ILS R&D Lab, LIG Nex1 Co., Ltd.) ;
- Kim, Sang-Moon (ILS R&D Lab, LIG Nex1 Co., Ltd.)
- Received : 2018.05.23
- Accepted : 2018.08.03
- Published : 2018.08.31
The development of weapon systems (or components) is hindered by the number of tests due to the limited development period and cost, which reduces the scale of accumulated data related to failures. Nevertheless, because a large amount of failure data and maintenance details during the operational period are managed by computerized data, the cause of failure of weapon systems (or components) can be analyzed using the data. On the other hand, analyzing the failure and maintenance details of various weapon systems is difficult because of the variation among groups and companies, and details of the cause of failure are described as unstructured text data. Fortunately, the recent developments of big data processing technology, machine learning algorithm, and improved HW computation ability have supported major research into various methods for processing the above unstructured data. In this paper, unstructured data related to the failure / maintenance of defense weapon systems (or components) is presented by applying doc2vec, a machine learning technique, to analyze the failure cases.
Machine learning;doc2vec;Clustering;Visualization;Failure mode;Weapon System
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