Quickest change detection, statistical learning and nonlinear filtering of jet engine data

喷气发动机数据的最快变化检测、统计学习和非线性过滤

• 批准号: 543433-2019
• 负责人: Namachchivaya, NavaratnamSri
• 金额: $ 5.83万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Collaborative Research and Development Grants
• 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=698694
• 关键词: Quickest; change; detection; statistical; learning

Modern jet engines are some of 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 engine performance parameters, from pressure and temperature of the engine gas path to vibration of the rotating components. The goals of the proposed collaborative research are to develop novel mathematical and statistical methods that exploit new sensing capabilities on engines for (i) constructing new anomaly-detection schemes and quickest-change detection algorithms; and (ii) discovering instability limits, thus preventing damage to engine components and lengthening their life-span. The research proposed here uses both model-based and data-driven theories to develop efficient numerical algorithms for quickest change detection, statistical learning and nonlinear filtering targeted at our specific problem. Theme 1, the proposed quickest-change detection, will be a vital procedure for engine performance monitoring. Once a change in an engine state has occurred, that change must be detected as soon as possible, while minimizing false detections. Theme 2 of the proposal focuses on the data-driven methods appropriate for data generated by jet engines. When no explicit dynamical model is available, system knowledge boils down to real-time measurements, possibly complemented by process history. Advances made in collaboration with the industry partner, TECSIS Corporation, will have a lasting impact on the Prognosis and Health Management (PHM) techniques for detecting abrupt changes or anomalies in gas turbine engines. The proposed data-centric methods will benefit the aerospace industry at large as well as open new research perspectives and domains that rely heavily on measurements for system monitoring and control.

现代喷气发动机是飞机上一些最昂贵的组件，设计为非常可靠的，成本高达2500万美元）。然而，他们仍然可以遇到意外的灾难性失败，从而导致悲剧和生命丧失。为了监视发动机的健康和性能，每个发动机都配备了约100个传感器，这些传感器可以测量发动机性能参数，从发动机气体路径的压力和温度到旋转组件的振动。拟议的协作研究的目标是开发新颖的数学和统计方法，以利用引擎上的新传感能力来构建新的异常检测方案和最快变化的检测算法； （ii）发现不稳定限制，从而防止发动机组件损坏并延长其寿命。这里提出的研究使用基于模型和数据驱动的理论来开发有效的数值算法，以最快的变化检测，统计学习和针对我们特定问题的非线性过滤。 主题1是提议的最快变化检测，将是发动机性能监视的重要程序。一旦发生了发动机状态的更改，就必须尽快检测到该变化，同时最大程度地减少虚假检测。该提案的主题2着重于适用于喷气发动机生成的数据的数据驱动方法。如果没有明确的动力学模型，系统知识就归结为实时测量，可能会得到过程历史记录的补充。与行业合作伙伴Tecsis Corporation合作取得的进步将对预后和健康管理（PHM）技术产生持久影响，以检测燃气轮机发动机的突然变化或异常。提出的以数据为中心的方法将使航空航天行业受益，以及很大程度上依赖于用于系统监控和控制的测量的开放新研究观点和领域。