Reliability Prediction Based on Dynamic Data Collected with Modern Technology

基于现代技术采集的动态数据的可靠性预测

• 批准号: 1068933
• 负责人: Yili Hong
• 金额: $ 21.02万
• 依托单位: Virginia Polytechnic Institute and State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-07-01 至 2014-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1068933&HistoricalAwards=false
• 关键词: Reliability; Prediction; Based; Dynamic; Data

The research objective of this award is to develop a general framework that can incorporate large-scale dynamic data to obtain more accurate reliability predictions. Modern technology, such as smart-chips, sensors and wireless networks, has changed data collection processes. There are more and more products installed with automatic data-collecting devices (ADCDs) which can dynamically record system performance, usage and environmental information for individual units in the field and/or transmit this information to data centers with owner's permissions. These products range from jet engines, wind turbines, power transformers and CAT scanners, to automobiles, copier machines and smart-phones. This research will first develop general models for incorporating dynamic data into predictions. Then methods will be developed for quantifying statistical uncertainties and advantage of using dynamic data. Sensitivity analysis will be conducted to assess model uncertainties. Computationally efficient algorithms and free software that is capable of processing large-scale datasets will also be developed. The developed methods will be validated with datasets from industrial and government partners.If successful, this research will provide a much-needed new paradigm for the arriving generation of field reliability data. In the near future when the cost of ADCDs further decreases, more and more products will be equipped with ADCDs. This research will have applications in various important areas such as manufacturing, renewable energy, and health care, because reliability information is critical for manufacturers to improve the competitive position of their products, and is also important for cost analysis, capital expenditures, and risk controls. The development of free software will make it possible for the developed methods to be widely disseminated. Graduate and undergraduate students from under-represented groups and women will be involved in this research. The integration of research with teaching will present students with modern reliability data analysis concepts and techniques.

该奖项的研究目标是开发一个通用框架，可以合并大规模动态数据以获得更准确的可靠性预测。智能芯片、传感器和无线网络等现代技术改变了数据收集过程。越来越多的产品安装了自动数据收集设备（ADCD），可以动态记录现场各个单元的系统性能、使用情况和环境信息和/或在所有者许可的情况下将该信息传输到数据中心。这些产品范围从喷气发动机、风力涡轮机、电力变压器和 CAT 扫描仪到汽车、复印机和智能手机。这项研究将首先开发将动态数据纳入预测的通用模型。然后将开发量化统计不确定性和使用动态数据的优势的方法。将进行敏感性分析以评估模型的不确定性。还将开发能够处理大规模数据集的计算高效算法和免费软件。所开发的方法将通过工业和政府合作伙伴的数据集进行验证。如果成功，这项研究将为现场可靠性数据的生成提供急需的新范例。不久的将来，当ADCD的成本进一步降低时，将会有越来越多的产品配备ADCD。这项研究将在制造、可再生能源和医疗保健等各个重要领域得到应用，因​​为可靠性信息对于制造商提高其产品的竞争地位至关重要，而且对于成本分析、资本支出和风险控制也很重要。自由软件的发展将使所开发的方法得以广泛传播。来自代表性不足群体的研究生和本科生以及女性将参与这项研究。研究与教学的结合将为学生提供现代可靠性数据分析概念和技术。