{"title":"基于机器学习和实验设计的枪管寿命退化分析","authors":"C. Drake, D. Ray","doi":"10.1109/RAMS48030.2020.9153585","DOIUrl":null,"url":null,"abstract":">The US Army M240 Small-Caliber Machine Gun Barrel degrades as round-count increases, leading to velocity drop and mismatch between aiming/fire control and round impact at range. This degradation can be understood as a functional response of velocity change over time, with time in this case represented by round-count on the barrel. Statistically modeling this type of functional response poses some additional challenges when compared to traditional one-dimensional data, and Functional Data Analysis (FDA) can help address this added complexity. Although FDA is not a new technique, its application to gun barrel degradation had never been employed before by the Department of Defense (DoD) until now.","PeriodicalId":360096,"journal":{"name":"2020 Annual Reliability and Maintainability Symposium (RAMS)","volume":"110 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Degradation Analysis of Gun Barrel Life Using Machine Learning and Design of Experiments\",\"authors\":\"C. Drake, D. Ray\",\"doi\":\"10.1109/RAMS48030.2020.9153585\",\"DOIUrl\":null,\"url\":null,\"abstract\":\">The US Army M240 Small-Caliber Machine Gun Barrel degrades as round-count increases, leading to velocity drop and mismatch between aiming/fire control and round impact at range. This degradation can be understood as a functional response of velocity change over time, with time in this case represented by round-count on the barrel. Statistically modeling this type of functional response poses some additional challenges when compared to traditional one-dimensional data, and Functional Data Analysis (FDA) can help address this added complexity. Although FDA is not a new technique, its application to gun barrel degradation had never been employed before by the Department of Defense (DoD) until now.\",\"PeriodicalId\":360096,\"journal\":{\"name\":\"2020 Annual Reliability and Maintainability Symposium (RAMS)\",\"volume\":\"110 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 Annual Reliability and Maintainability Symposium (RAMS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/RAMS48030.2020.9153585\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 Annual Reliability and Maintainability Symposium (RAMS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/RAMS48030.2020.9153585","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Degradation Analysis of Gun Barrel Life Using Machine Learning and Design of Experiments
>The US Army M240 Small-Caliber Machine Gun Barrel degrades as round-count increases, leading to velocity drop and mismatch between aiming/fire control and round impact at range. This degradation can be understood as a functional response of velocity change over time, with time in this case represented by round-count on the barrel. Statistically modeling this type of functional response poses some additional challenges when compared to traditional one-dimensional data, and Functional Data Analysis (FDA) can help address this added complexity. Although FDA is not a new technique, its application to gun barrel degradation had never been employed before by the Department of Defense (DoD) until now.