CAREER: Real-Time Degradation-Based Prognostic Methodology for Improving Reliability and Maintenance Logistics

职业：基于实时退化的预测方法，用于提高可靠性和维护物流

• 批准号: 0738647
• 负责人: Nagi Gebraeel
• 金额: $ 40万
• 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: 2007
• 资助国家: 美国
• 起止时间: 2007-07-01 至 2013-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0738647&HistoricalAwards=false
• 关键词: CAREER; Real; Time; Degradation; Based

This Faculty Early Career Development (CAREER) award provides funding for developing a sensory-based prognostic methodology for predicting failures of complex systems, such as manufacturing systems, nuclear plants, military systems, and others. The proposed methodology will integrate system-specific information acquired through sensory monitoring technology, and the general reliability and durability characteristics of a system. The reliability knowledge will provide preliminary failure time estimates. Real-time system-specific information, in the form of degradation-based sensory signals, will be used to continuously update these estimates and provide accurate remaining life predictions based on the unique performance of the system and its components. This will be achieved be correlating the evolution of the degradation signals with the underlying physical transitions that occur prior to failure. The proposed methodology also captures the effects of time-varying operating and environmental conditions on degradation and failure processes. These dynamically evolving remaining life predictions will then be integrated with maintenance and spare parts logistics models to provide a factual sense and respond paradigm. These developments will be validated using laboratory testing platforms, industrial case studies that include construction and agricultural equipment, electronics, and avionics systems, and military applications that include Navy aircraft power systems and others.The research developments target the prevention of unexpected failures. The success of this research will positively impact the effectiveness of engineering systems in the manufacturing and service sectors, health care, and national security, among others. The research will support ongoing national initiatives like the "Next Generation Manufacturing Technology Initiative". Advances in "biosensors" will enable the implementation of these findings in health care applications and will improve human safety. From an educational standpoint, the infusion of the proposed developments into the curriculum will present graduate and undergraduate students with a contemporary view of reliability and maintenance logistics, and will provide a rich variety of research opportunities.

该学院早期职业发展（CAREER）奖为开发基于感觉的预测方法提供资金，用于预测复杂系统的故障，如制造系统，核电厂，军事系统等。 拟议的方法将整合通过传感器监测技术获得的系统特定信息，以及系统的一般可靠性和耐久性特征。 可靠性知识将提供初步的故障时间估计。 实时系统特定信息，以基于退化的感觉信号的形式，将用于不断更新这些估计，并根据系统及其组件的独特性能提供准确的剩余寿命预测。 这将通过将劣化信号的演变与失效之前发生的潜在物理转变相关联来实现。 所提出的方法还捕捉到随时间变化的操作和环境条件对退化和故障过程的影响。 这些动态演变的剩余寿命预测将与维护和备件物流模型相结合，以提供一个事实的意义和响应范例。 这些发展将通过实验室测试平台、工业案例研究（包括建筑和农业设备、电子和航空电子系统）以及军事应用（包括海军飞机动力系统等）进行验证。研究发展的目标是预防意外故障。 这项研究的成功将对制造业和服务业、医疗保健和国家安全等领域的工程系统的有效性产生积极影响。 该研究将支持正在进行的国家倡议，如“下一代制造技术倡议”。 “生物传感器”的进步将使这些发现在医疗保健应用中得以实施，并将改善人类安全。 从教育的角度来看，注入拟议的发展到课程将目前的研究生和本科生的可靠性和维护物流的当代观点，并将提供各种各样的研究机会。