Reliability analysis and maintenance optimization based on advanced prognostics methodologies

可靠性衡量的是部件或系统执行其预期功能的能力，它是飞机系统、制造系统和风力发电系统等工程系统最关键的性能指标之一。维修优化的目的是提高可靠性，降低总体成本。在传统的可靠性分析中，失效数据主要是用统计方法分析的，可靠性性能是按部件总体而不是单个单元来估计的。状态监测数据的可用性，如振动和声学数据，使获得单个部件的更准确的可靠性预测成为可能，并具有随后实现更有效的系统可靠性分析和维护管理的巨大潜力。这一研究计划的长期目标是开发一套完整的框架和一套方法和工具，用于基于准确的预测信息进行准确的可靠性分析和有效的维护和物流优化，并将其应用于工程系统，特别是可再生能源、制造和航空航天系统。其近期目标是大力推进极具发展前景的综合预报，使其方法和工具能够广泛应用于工业中的复杂设备，建立统一的基于部件预报信息的系统可靠性分析框架和方法，产生更准确、更高效的煤层气优化方法，使其适用于复杂系统，并针对其特殊的时变负荷特性和复杂的结构，开发有效的提高风能系统可靠性的方法。这项研究的结果将提高工程系统的可靠性、安全性和成本效益，并为加拿大的可再生能源、制造业、航空航天和石油和天然气行业带来重大经济效益。将培养高素质的人才。

Reliability measures the ability of a component or system performing its intended functions, and it is one of the most critical performance measures of engineering systems such as aircraft systems, manufacturing systems and wind power systems. Maintenance optimization aims to improve reliability and reduce the overall cost. In conventional reliability analysis, failure data are mainly analyzed using statistical methods, and reliability performance is estimated for component population rather than individual units. The availability of condition monitoring data, such as vibration and acoustic data, make it possible to obtain a far more accurate reliability prediction for individual components, and there's great potential to subsequently achieve more effective system reliability analysis and maintenance management. The long-term objective of this research program is to develop an integrated framework and a suite of methods and tools for accurate reliability analysis and effective maintenance and logistics optimization based on accurate prognostics information, and apply them to engineering systems particularly renewable energy, manufacturing, and aerospace systems. The short-term objectives are to greatly advance the highly promising integrated prognostics so that its methods and tools can be applied to complex equipments in broad industry applications, establish a unified system reliability analysis framework and methods based on component prognostics information, generate more accurate and efficient CBM optimization methods so that they are applicable to complex systems, and develop effective methods for wind energy system reliability enhancement considering their special time-varying loading characteristics and complex structures. Results from this research will improve reliability, safety and cost-effectiveness of engineering systems, and generate major economic benefits for Canadian renewable energy, manufacturing, aerospace, and oil and gas industries. Highly qualified personnel will be trained.