Mixed-Model Fault Diagnosis

混合模型故障诊断

• 批准号: 9971172
• 负责人: Tracy Larrabee
• 金额: $ 21.31万
• 依托单位: University of California-Santa Cruz
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1999
• 资助国家: 美国
• 起止时间: 1999-08-01 至 2005-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9971172&HistoricalAwards=false
• 关键词: Mixed; Model; Fault; Diagnosis

This research is an investigation of model-independent fault diagnosis systems that are intended to be robust, comprehensive, extendible, andpractical. The approach is to exploit the underlying probabilisticframework of the system, which makes it possible to incorporate disparatediagnostic algorithms, different sets of data, and a mixture of faultmodels into a single diagnostic result. The use of heterogeneous faultcandidates will allow a diagnosis result that pinpoints problem areas basedon data gleaned from the results of logic tests, delay tests, and IDDQtests. The research results are being verified by testing them using actualindustrial IC failure data. Algorithms and software demonstrating theresearch ideas are being developed.

本研究旨在探讨模型无关的故障诊断系统，其目的在于使系统具有鲁棒性、全面性、可扩展性和实用性。该方法是利用潜在的probabilisticframework的系统，这使得有可能将consideratediagnostic算法，不同的数据集，和混合faultmodels到一个单一的诊断结果。使用异构faultcandidates将允许一个诊断结果，pinpoints问题领域basedon从逻辑测试，延迟测试和IDDQ测试的结果收集的数据。通过实际工业IC失效数据对研究结果进行了验证。算法和软件演示的研究思路正在开发中。