Reliability Assurance Methodology Incorporating Advanced Diagnostics and Prognostics

结合先进诊断和预测的可靠性保证方法

• 批准号: RGPIN-2015-04897
• 负责人: Zuo, Mingjian
• 金额: $ 3.93万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=613238
• 关键词: Reliability; Assurance; Methodology; Incorporating; Advanced

Today’s society depends on the reliable, uninterrupted operation of colossal engineering systems such as power grids, telecommunication networks, pipelines, and air, ground, and water transportation systems. Despite important advancements in reliability assurance methodologies over the past 70 years, accidents and major service disruptions affecting millions of people still occur frequently. The fact that deterioration occurs as a device is used and inherent uncertainty exists in material properties and degradation behaviors makes traditional time-based reliability assurance methods inadequate. Recent thrusts of the reliability research community are to incorporate condition monitoring and advanced diagnostics and prognostics into online reliability assurance of sophisticated engineering systems. This research program aims to develop effective health indicators of equipment deterioration by conducting dynamic simulation and laboratory experiments on a gearbox test rig, develop information fusion approaches to make inferences on the hidden health state of the equipment, estimate the remaining useful life distribution of a running system utilizing information on health indicators and inferred health states, evaluate system health state distribution based on component state distributions and dependency relationships among components, and develop dynamic maintenance optimization approaches to ensure safe, reliable and cost-effective operation of engineering systems. The proposed research will generate fundamental theorems, efficient algorithms, and practical models for detection and identification of impending failure modes, for reliability evaluation of multi-state systems, for prediction of the remaining useful life of running equipment, and for dynamic operation and maintenance decision making of engineering systems. The results to be generated have excellent potential for applications in design, manufacture, and operation of modern engineering systems such as pipelines, petro-chemical refineries, power generators, telecommunication networks, power grids, and transportation vehicles. The results of this research can be applied in industrial settings to benefit the Canadian industries directly. Highly qualified personnel will be trained for the Canadian industries.

当今社会依赖于庞大的工程系统的可靠、不间断运行，如电网、电信网络、管道以及空中、地面和水上运输系统。尽管在过去70年中可靠性保证方法取得了重要进展，但影响数百万人的事故和重大服务中断仍然频繁发生。随着设备的使用而发生劣化，并且材料特性和劣化行为存在固有的不确定性，这一事实使得传统的基于时间的可靠性保证方法不足。可靠性研究界最近的重点是将状态监测和先进的诊断和故障诊断纳入复杂工程系统的在线可靠性保证。本研究计划旨在通过在齿轮箱试验台上进行动态仿真和实验室实验，开发有效的设备劣化健康指标，开发信息融合方法以推断设备的隐藏健康状态，利用健康指标和推断健康状态的信息估计运行系统的剩余使用寿命分布，基于组件状态分布和组件间依赖关系评估系统健康状态分布，并开发动态维护优化方法，以确保工程系统安全、可靠和经济有效地运行。所提出的研究将产生的基本定理，有效的算法，和实用的模型，检测和识别即将发生的故障模式，多状态系统的可靠性评估，运行设备的剩余使用寿命的预测，以及工程系统的动态运行和维护决策。所产生的结果在设计，制造和现代工程系统，如管道，石化炼油厂，发电机，电信网络，电网和运输车辆的操作中具有很好的应用潜力。这项研究的结果可以应用在工业环境中，使加拿大工业直接受益。将为加拿大工业培训高素质的人员。