{"title":"带有潜在故障的修正浴盆曲线","authors":"J. English, Li Yan, T. L. Landers","doi":"10.1109/RAMS.1995.513249","DOIUrl":null,"url":null,"abstract":"Burn-in and stress screening are becoming increasingly popular in the commercial electronics industry as customers become increasingly sensitive to failures occurring in the useful life of a product or system. For example, thermal stress screening (TSS) is an assembly-level electronics manufacturing process that evolved from the burn-in processes used in NASA and DoD programs. While burn-in subjects the product to expected field extremes to expose infant mortalities (latent failures), TSS briefly exposes a product to fast temperature rate-of-change and out-of-spec temperatures to trigger failures that would otherwise occur during the useful life of the product. In support of this known failure behavior, the classical bathtub curve should be modified to aid in the economic modeling of various screen types. We have conducted extensive modeling efforts that have resulted in a systematic approach to explicitly modeling the latent failures in the bathtub curve. In this paper, we describe the efforts that have been dedicated to model the latent failures known to exist in many products and systems. The resulting failure distribution is a truncated, mixed Weibull distribution. This model is proving to be an effective and relatively simple means to model the complex nature of failures of a system. With this increased flexibility, we can measure the impact of stress screens in varying conditions and ultimately design optimal screens.","PeriodicalId":143102,"journal":{"name":"Annual Reliability and Maintainability Symposium 1995 Proceedings","volume":"30 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1995-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"A modified bathtub curve with latent failures\",\"authors\":\"J. English, Li Yan, T. L. Landers\",\"doi\":\"10.1109/RAMS.1995.513249\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Burn-in and stress screening are becoming increasingly popular in the commercial electronics industry as customers become increasingly sensitive to failures occurring in the useful life of a product or system. For example, thermal stress screening (TSS) is an assembly-level electronics manufacturing process that evolved from the burn-in processes used in NASA and DoD programs. While burn-in subjects the product to expected field extremes to expose infant mortalities (latent failures), TSS briefly exposes a product to fast temperature rate-of-change and out-of-spec temperatures to trigger failures that would otherwise occur during the useful life of the product. In support of this known failure behavior, the classical bathtub curve should be modified to aid in the economic modeling of various screen types. We have conducted extensive modeling efforts that have resulted in a systematic approach to explicitly modeling the latent failures in the bathtub curve. In this paper, we describe the efforts that have been dedicated to model the latent failures known to exist in many products and systems. The resulting failure distribution is a truncated, mixed Weibull distribution. This model is proving to be an effective and relatively simple means to model the complex nature of failures of a system. With this increased flexibility, we can measure the impact of stress screens in varying conditions and ultimately design optimal screens.\",\"PeriodicalId\":143102,\"journal\":{\"name\":\"Annual Reliability and Maintainability Symposium 1995 Proceedings\",\"volume\":\"30 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1995-01-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Annual Reliability and Maintainability Symposium 1995 Proceedings\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/RAMS.1995.513249\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annual Reliability and Maintainability Symposium 1995 Proceedings","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/RAMS.1995.513249","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Burn-in and stress screening are becoming increasingly popular in the commercial electronics industry as customers become increasingly sensitive to failures occurring in the useful life of a product or system. For example, thermal stress screening (TSS) is an assembly-level electronics manufacturing process that evolved from the burn-in processes used in NASA and DoD programs. While burn-in subjects the product to expected field extremes to expose infant mortalities (latent failures), TSS briefly exposes a product to fast temperature rate-of-change and out-of-spec temperatures to trigger failures that would otherwise occur during the useful life of the product. In support of this known failure behavior, the classical bathtub curve should be modified to aid in the economic modeling of various screen types. We have conducted extensive modeling efforts that have resulted in a systematic approach to explicitly modeling the latent failures in the bathtub curve. In this paper, we describe the efforts that have been dedicated to model the latent failures known to exist in many products and systems. The resulting failure distribution is a truncated, mixed Weibull distribution. This model is proving to be an effective and relatively simple means to model the complex nature of failures of a system. With this increased flexibility, we can measure the impact of stress screens in varying conditions and ultimately design optimal screens.