{"title":"基于卷积神经网络深度学习的智能工厂剩余使用寿命估计","authors":"Jehn-Ruey Jiang, Chang-Kuei Kuo","doi":"10.1109/ICICE.2017.8478928","DOIUrl":null,"url":null,"abstract":"Estimating the remaining useful life (RUL) of machines or components is essential for prognostics and health management (PHM) in smart factories. This paper enhances the convolutional neural network (CNN) deep learning for RUL estimation in smart factory applications. The enhanced CNN deep learning is applied to NASA C-MAPSS (Commercial Modular Aero-Propulsion System Simulation) data set to estimate the RUL of aero-propulsion engines. It is shown to have better performance than other related methods.","PeriodicalId":233396,"journal":{"name":"2017 International Conference on Information, Communication and Engineering (ICICE)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Enhancing Convolutional Neural Network Deep Learning for Remaining Useful Life Estimation in Smart Factory Applications\",\"authors\":\"Jehn-Ruey Jiang, Chang-Kuei Kuo\",\"doi\":\"10.1109/ICICE.2017.8478928\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Estimating the remaining useful life (RUL) of machines or components is essential for prognostics and health management (PHM) in smart factories. This paper enhances the convolutional neural network (CNN) deep learning for RUL estimation in smart factory applications. The enhanced CNN deep learning is applied to NASA C-MAPSS (Commercial Modular Aero-Propulsion System Simulation) data set to estimate the RUL of aero-propulsion engines. It is shown to have better performance than other related methods.\",\"PeriodicalId\":233396,\"journal\":{\"name\":\"2017 International Conference on Information, Communication and Engineering (ICICE)\",\"volume\":\"37 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 International Conference on Information, Communication and Engineering (ICICE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICICE.2017.8478928\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 International Conference on Information, Communication and Engineering (ICICE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICICE.2017.8478928","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
摘要
评估机器或部件的剩余使用寿命(RUL)对于智能工厂的预测和健康管理(PHM)至关重要。本文对卷积神经网络(CNN)深度学习在智能工厂应用中的RUL估计进行了改进。将增强的CNN深度学习应用于NASA C-MAPSS (Commercial Modular Aero-Propulsion System Simulation)数据集,估计航空推进发动机的RUL。与其他相关方法相比,该方法具有更好的性能。
Enhancing Convolutional Neural Network Deep Learning for Remaining Useful Life Estimation in Smart Factory Applications
Estimating the remaining useful life (RUL) of machines or components is essential for prognostics and health management (PHM) in smart factories. This paper enhances the convolutional neural network (CNN) deep learning for RUL estimation in smart factory applications. The enhanced CNN deep learning is applied to NASA C-MAPSS (Commercial Modular Aero-Propulsion System Simulation) data set to estimate the RUL of aero-propulsion engines. It is shown to have better performance than other related methods.