{"title":"贝叶斯神经网络武器系统能否改善预测性维护？","authors":"Michael L. Potter, Miru D. Jun","doi":"10.1109/RAMS51492.2024.10457828","DOIUrl":null,"url":null,"abstract":"We implement a Bayesian inference process for Neural Networks to model the time to failure of highly reliable weapon systems with interval-censored data and time-varying covariates. We analyze and benchmark our approach, LaplaceNN, on synthetic and real datasets with standard classification metrics such as Receiver Operating Characteristic (ROC) Area Under Curve (AUC) Precision-Recall (PR) AUC, and reliability curve visualizations.","PeriodicalId":518362,"journal":{"name":"2024 Annual Reliability and Maintainability Symposium (RAMS)","volume":"18 5","pages":"1-7"},"PeriodicalIF":0.0000,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Do Bayesian Neural Networks Weapon System Improve Predictive Maintenance?\",\"authors\":\"Michael L. Potter, Miru D. Jun\",\"doi\":\"10.1109/RAMS51492.2024.10457828\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We implement a Bayesian inference process for Neural Networks to model the time to failure of highly reliable weapon systems with interval-censored data and time-varying covariates. We analyze and benchmark our approach, LaplaceNN, on synthetic and real datasets with standard classification metrics such as Receiver Operating Characteristic (ROC) Area Under Curve (AUC) Precision-Recall (PR) AUC, and reliability curve visualizations.\",\"PeriodicalId\":518362,\"journal\":{\"name\":\"2024 Annual Reliability and Maintainability Symposium (RAMS)\",\"volume\":\"18 5\",\"pages\":\"1-7\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-01-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2024 Annual Reliability and Maintainability Symposium (RAMS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/RAMS51492.2024.10457828\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2024 Annual Reliability and Maintainability Symposium (RAMS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/RAMS51492.2024.10457828","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Do Bayesian Neural Networks Weapon System Improve Predictive Maintenance?
We implement a Bayesian inference process for Neural Networks to model the time to failure of highly reliable weapon systems with interval-censored data and time-varying covariates. We analyze and benchmark our approach, LaplaceNN, on synthetic and real datasets with standard classification metrics such as Receiver Operating Characteristic (ROC) Area Under Curve (AUC) Precision-Recall (PR) AUC, and reliability curve visualizations.
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