{"title":"电子安全设备的统计过程控制试验","authors":"D. W. Murray, D. D. Spencer","doi":"10.1109/CCST.1994.363801","DOIUrl":null,"url":null,"abstract":"Statistical Process Control testing of manufacturing processes began back in the 1940's with the development of Process Control Charts by Dr. Waiter A. Shewart. Sandia National Laboratories has developed an application of the SPC method for performance testing of electronic security equipment. This paper documents the evaluation of this testing methodology applied to electronic security equipment and an associated laptop computer-based system for obtaining and analyzing the test data. Sandia developed this SPC sensor performance testing method primarily for use on portal metal detectors, but, has evaluated it for testing of an exterior intrusion detection sensor and other electronic security devices. This method is an alternative to the traditional binomial (alarm or no-alarm) performance testing. The limited amount of information in binomial data drives the number of tests necessary to meet regulatory requirements to unnecessarily high levels. For example, a requirement of a 0.85 probability of detection with a 90% confidence requires a minimum of 19 alarms out of 19 trials. By extracting and analyzing measurement (variables) data whenever possible instead of the more typical binomial data, the user becomes more informed about equipment health with fewer tests (as low as five per periodic evaluation).<<ETX>>","PeriodicalId":314758,"journal":{"name":"1994 Proceedings of IEEE International Carnahan Conference on Security Technology","volume":"302 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1994-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Statistical process control testing of electronic security equipment\",\"authors\":\"D. W. Murray, D. D. Spencer\",\"doi\":\"10.1109/CCST.1994.363801\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Statistical Process Control testing of manufacturing processes began back in the 1940's with the development of Process Control Charts by Dr. Waiter A. Shewart. Sandia National Laboratories has developed an application of the SPC method for performance testing of electronic security equipment. This paper documents the evaluation of this testing methodology applied to electronic security equipment and an associated laptop computer-based system for obtaining and analyzing the test data. Sandia developed this SPC sensor performance testing method primarily for use on portal metal detectors, but, has evaluated it for testing of an exterior intrusion detection sensor and other electronic security devices. This method is an alternative to the traditional binomial (alarm or no-alarm) performance testing. The limited amount of information in binomial data drives the number of tests necessary to meet regulatory requirements to unnecessarily high levels. For example, a requirement of a 0.85 probability of detection with a 90% confidence requires a minimum of 19 alarms out of 19 trials. By extracting and analyzing measurement (variables) data whenever possible instead of the more typical binomial data, the user becomes more informed about equipment health with fewer tests (as low as five per periodic evaluation).<<ETX>>\",\"PeriodicalId\":314758,\"journal\":{\"name\":\"1994 Proceedings of IEEE International Carnahan Conference on Security Technology\",\"volume\":\"302 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1994-10-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"1994 Proceedings of IEEE International Carnahan Conference on Security Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CCST.1994.363801\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"1994 Proceedings of IEEE International Carnahan Conference on Security Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CCST.1994.363801","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
摘要
生产过程的统计过程控制测试始于20世纪40年代,由Dr. Waiter A. Shewart开发了过程控制图。桑迪亚国家实验室开发了一种SPC方法应用于电子安全设备的性能测试。本文记录了该测试方法的评估,该测试方法适用于电子安全设备和相关的基于笔记本电脑的系统,用于获取和分析测试数据。桑迪亚公司开发了这种SPC传感器性能测试方法,主要用于门户金属探测器,但也对外部入侵检测传感器和其他电子安全设备的测试进行了评估。这种方法是传统的二项(报警或无报警)性能测试的替代方法。二项数据中有限的信息量使满足法规要求所需的测试数量达到不必要的高水平。例如,如果检测概率为0.85,置信度为90%,则要求在19次试验中至少有19次报警。通过尽可能提取和分析测量(变量)数据,而不是更典型的二项数据,用户可以通过更少的测试(每次定期评估低至5次)更了解设备的健康状况
Statistical process control testing of electronic security equipment
Statistical Process Control testing of manufacturing processes began back in the 1940's with the development of Process Control Charts by Dr. Waiter A. Shewart. Sandia National Laboratories has developed an application of the SPC method for performance testing of electronic security equipment. This paper documents the evaluation of this testing methodology applied to electronic security equipment and an associated laptop computer-based system for obtaining and analyzing the test data. Sandia developed this SPC sensor performance testing method primarily for use on portal metal detectors, but, has evaluated it for testing of an exterior intrusion detection sensor and other electronic security devices. This method is an alternative to the traditional binomial (alarm or no-alarm) performance testing. The limited amount of information in binomial data drives the number of tests necessary to meet regulatory requirements to unnecessarily high levels. For example, a requirement of a 0.85 probability of detection with a 90% confidence requires a minimum of 19 alarms out of 19 trials. By extracting and analyzing measurement (variables) data whenever possible instead of the more typical binomial data, the user becomes more informed about equipment health with fewer tests (as low as five per periodic evaluation).<>