Huairui Guo, A. Mettas, G. Sarakakis, Pengying Niu
{"title":"具有最大似然估计的可修系统分段NHPP模型","authors":"Huairui Guo, A. Mettas, G. Sarakakis, Pengying Niu","doi":"10.1109/RAMS.2010.5448029","DOIUrl":null,"url":null,"abstract":"Non-homogeneous Poisson process (NHPP) models are widely used for repairable system analysis. Different NHPP models have been developed for different applications. It has been noticed that almost all the existing models apply only a single model for the entire system development or operation period. However, in some circumstances, such as when the system design or the system operation environment experiences major changes, a single model will not be appropriate to describe the failure behavior for the entire timeline. In this paper, we proposed a piecewise NHPP model for repairable systems with multiple stages. The maximum likelihood estimation (MLE) for the model parameters is also provided.","PeriodicalId":299782,"journal":{"name":"2010 Proceedings - Annual Reliability and Maintainability Symposium (RAMS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":"{\"title\":\"Piecewise NHPP models with maximum likelihood estimation for repairable systems\",\"authors\":\"Huairui Guo, A. Mettas, G. Sarakakis, Pengying Niu\",\"doi\":\"10.1109/RAMS.2010.5448029\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Non-homogeneous Poisson process (NHPP) models are widely used for repairable system analysis. Different NHPP models have been developed for different applications. It has been noticed that almost all the existing models apply only a single model for the entire system development or operation period. However, in some circumstances, such as when the system design or the system operation environment experiences major changes, a single model will not be appropriate to describe the failure behavior for the entire timeline. In this paper, we proposed a piecewise NHPP model for repairable systems with multiple stages. The maximum likelihood estimation (MLE) for the model parameters is also provided.\",\"PeriodicalId\":299782,\"journal\":{\"name\":\"2010 Proceedings - Annual Reliability and Maintainability Symposium (RAMS)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-04-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"12\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 Proceedings - Annual Reliability and Maintainability Symposium (RAMS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/RAMS.2010.5448029\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 Proceedings - Annual Reliability and Maintainability Symposium (RAMS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/RAMS.2010.5448029","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Piecewise NHPP models with maximum likelihood estimation for repairable systems
Non-homogeneous Poisson process (NHPP) models are widely used for repairable system analysis. Different NHPP models have been developed for different applications. It has been noticed that almost all the existing models apply only a single model for the entire system development or operation period. However, in some circumstances, such as when the system design or the system operation environment experiences major changes, a single model will not be appropriate to describe the failure behavior for the entire timeline. In this paper, we proposed a piecewise NHPP model for repairable systems with multiple stages. The maximum likelihood estimation (MLE) for the model parameters is also provided.