Novel Data-Centric Methods for Inference and Prediction of Large-Scale Complex Systems

用于大规模复杂系统推理和预测的以数据为中心的新方法

• 批准号: RGPIN-2018-03735
• 负责人: Namachchivaya, Navaratnam
• 金额: $ 4.66万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=743689
• 关键词: Novel; Data; Centric; Methods; Inference

Modern jet engines are the most expensive components on an aircraft and are engineered to be extremely reliable at great cost (up to $25 million per engine). Yet they can still experience unexpected catastrophic failure, resulting in tragedy and loss of life. In order to monitor engine health and performance, each engine is equipped with about 100 sensors that measure a variety of performance parameters, from the pressure and temperature of the engine gas path to vibration of the rotating components. Stall and Surge are dynamic instabilities in which the engine compressor adds energy to small oscillations in the system, increasing their amplitude, potentially causing damage to engine components. In order to prevent damage, compressor instability must be controlled, which requires state estimators (filters), for example, to estimate the mass flow rate from the pressure measurement. The goals of the proposed research are to develop novel mathematical and statistical methods that exploit new sensing capabilities on engines for (i) determining instability limits; (ii) developing advanced reduced-order filters based on nonlinear models with uncertainties; and (iii) developing new anomaly-detection schemes and quickest-change detection algorithms.Theme 1 of the proposal focuses on the dynamics and control of compressor instabilities based on a full partial differential equation (PDE) model with uncertainty. A number of obstacles must be resolved to advance filtering of nonlinear PDE models. A key issue is computational complexity, which requires model-order reduction techniques to enable efficient processing and data assimilation. The filtering strategy proposed here will lead to better feedback control of compressor instabilities, thus preventing damage to engine components and lengthening their life-span.Theme 2, the proposed quickest-change detection, will be a vital procedure for engine performance monitoring. Engine data are obtained sequentially: as long as the engine is categorized as being in a ``normal state," its operation continues unabated. However, once a change in state has occurred, that change must be detected as soon as possible, while minimizing false detections. The statistical methods proposed, which involve optimizing the tradeoff between a measure of detection delay and a measure of the frequency of false alarms, will yield new means for understanding the behavior of large-scale complex systems at a higher level of sophistication.The proposed data-centric methods will also open new research perspectives and domains that rely heavily on measurements for system monitoring and control. Finally, the broad educational impacts of this proposal include cross-disciplinary training of graduate students in a variety of mathematical, statistical and computational techniques and in some of the most complex technologies built, and international collaboration.

现代喷气发动机是飞机上最昂贵的部件，并且以高昂的成本（每台发动机高达2500万美元）设计得极其可靠。然而，他们仍然可能经历意想不到的灾难性失败，导致悲剧和生命损失。为了监测发动机的健康状况和性能，每台发动机都配备了大约100个传感器，用于测量各种性能参数，从发动机气路的压力和温度到旋转部件的振动。失速和喘振是动态不稳定性，发动机压气机为系统中的小振荡增加能量，增加其振幅，可能对发动机部件造成损坏。为了防止损坏，必须控制压缩机的不稳定，这就需要状态估计器（过滤器），例如，从压力测量中估计质量流量。拟议研究的目标是开发新的数学和统计方法，利用发动机的新传感能力，用于(i)确定不稳定性极限；（ii）基于不确定非线性模型开发先进的降阶滤波器；（三）开发新的异常检测方案和快速变化检测算法。本文的主题1是基于不确定性全偏微分方程（PDE）模型的压缩机不稳定性动力学与控制。要对非线性偏微分方程模型进行超前滤波，必须克服许多障碍。一个关键问题是计算复杂性，这需要模型降阶技术来实现有效的处理和数据同化。本文提出的滤波策略可以更好地对压气机不稳定性进行反馈控制，从而防止发动机部件的损坏，延长发动机部件的使用寿命。主题2，提出的快速变化检测，将是发动机性能监测的一个重要程序。引擎数据是按顺序获得的：只要引擎被归类为“正常状态”，它的运行就会持续下去。但是，一旦状态发生变化，必须尽快检测到该变化，同时尽量减少错误检测。所提出的统计方法，涉及到在检测延迟度量和假警报频率度量之间的优化权衡，将产生新的方法，用于在更高的复杂水平上理解大规模复杂系统的行为。提出的以数据为中心的方法也将开辟新的研究视角和领域，严重依赖于系统监测和控制的测量。最后，该提案的广泛教育影响包括对研究生的跨学科培训，包括各种数学、统计和计算技术，以及一些最复杂的技术，以及国际合作。