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: $ 2.33万
• 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: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=712528
• 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，其设计非常可靠，成本很高（每台发动机高达2500万美元）。然而，它们仍然可能经历意想不到的灾难性故障，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，其中发动机压缩机将能量添加到系统中的小振荡，增加其振幅，可能导致发动机部件损坏。为了防止损坏，必须控制压缩机的不稳定性，这需要状态估计器（滤波器），例如，以根据压力测量来估计质量流率。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）开发基于不确定性非线性模型的高级降阶滤波器;（iii）开发新的异常检测方案和快速变化检测算法。 该提案的主题1侧重于基于不确定性的全偏微分方程（PDE）模型的压缩机不稳定性的动力学和控制。为了改进非线性偏微分方程模型的滤波，必须解决一些障碍。一个关键的问题是计算的复杂性，这需要模型阶数减少技术，以实现有效的处理和数据同化。本文提出的滤波策略将导致更好的压气机不稳定性的反馈控制，从而防止损坏发动机部件并延长其寿命。 主题2，建议的快速变化检测，将是一个重要的程序，发动机性能监控。发动机数据是按顺序获得的：只要发动机被归类为处于"正常状态”，其运行就不会减弱。然而，一旦状态发生变化，必须尽快检测到该变化，同时最大限度地减少错误检测。所提出的统计方法，其中涉及优化检测延迟的措施和虚警的频率的措施之间的权衡，将产生新的手段来理解大规模复杂系统的行为在一个更高的复杂程度。 所提出的以数据为中心的方法还将开辟新的研究视角和领域，这些领域在很大程度上依赖于系统监测和控制的测量。最后，这一建议的广泛教育影响包括研究生在各种数学，统计和计算技术以及一些最复杂的技术方面的跨学科培训，以及国际合作。