Improving Reliability Analysis with System Data from Operating Environments

利用操作环境中的系统数据改进可靠性分析

• 批准号: 9812868
• 负责人: Paul Kvam
• 金额: $ 12.09万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1998
• 资助国家: 美国
• 起止时间: 1998-10-01 至 2001-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9812868&HistoricalAwards=false
• 关键词: Improving; Reliability; Analysis; System; Data

9812868 Kvam In industries that cannot afford extensive testing programs to ensure product reliability and effectiveness, operational data is necessary for product reliability assessment. This grant supports the development of statistical methods for deducing component reliability while the component is working in an operational system. Typical systems considered include k-out-of-n systems for which k or more working components are needed to maintain the n-component system. Research centers on analyzing data from operating systems in nonstandard (i.e., non-laboratory) conditions, including (1) censored or truncated data, (2) systems that are repaired imperfectly, (3) component groups with known stochastic differences, such as one component having higher average reliability than another, and (4) components that have known reliability properties, such as a failure rate that is known to increase with time or use. For example, with systems that are repaired imperfectly, a maintenance policy that dictates when old systems or components must be replaced with newer ones is critical. Naturally, this maintenance policy will be greatly affected if the failure rate of the components is known to increase with system use. A primary application for the statistical methods developed from this grant are from the nuclear industry, where components are so reliable that component testing leads to few or no failures. In many nuclear systems, component redundancy is used to ensure high overall reliability, and system operation can sometimes mask individual component data. Censoring, imperfect repair, and order restrictions often combine in this environment to make inference problems difficult. As another application, producers in the software industry risk having their products become obsolete during prolonged testing programs, thus data from day to day operation may be necessary to augment accelerated life testing and other planned analyses of software reliability.

9812868 克瓦姆 在无法承担广泛的测试计划，以确保产品的可靠性和有效性，运行数据是必要的产品可靠性评估的行业。该补助金支持开发统计方法，以推断组件在操作系统中工作时的组件可靠性。所考虑的典型系统包括k/n系统，对于该系统，需要k个或更多个工作部件来维持n部件系统。研究集中在分析来自非标准操作系统的数据（即，非实验室）条件，包括（1）删失或截断的数据，（2）不完全修复的系统，（3）具有已知随机差异的部件组，例如一个部件具有比另一个部件更高的平均可靠性，以及（4）具有已知可靠性特性的部件，例如已知随时间或使用而增加的故障率。 例如，对于修复不完善的系统，规定何时必须用新系统或组件替换旧系统或组件的维护策略至关重要。当然，如果已知组件的故障率随着系统的使用而增加，则这种维护策略将受到很大影响。 从该补助金开发的统计方法的主要应用是从核工业，其中组件是如此可靠，组件测试导致很少或没有失败。在许多核系统中，组件冗余用于确保高的整体可靠性，并且系统操作有时会屏蔽单个组件数据。在这种环境中，删失、不完美修复和顺序限制经常联合收割机结合在一起，使推理问题变得困难。作为另一种应用，软件行业的生产商在长期测试计划中冒着产品过时的风险，因此，日常操作的数据可能是必要的，以增强加速寿命测试和其他计划的软件可靠性分析。