DDDAS - SMRP: A Framework For the Dynamic Data-Driven Fault Diagnosis of Wind Turbine Systems

DDDAS - SMRP：风力涡轮机系统动态数据驱动故障诊断框架

• 批准号: 0540132
• 负责人: Yu Ding
• 金额: $ 18万
• 依托单位: Texas A&M Engineering Experiment Station
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-06-01 至 2011-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0540132&HistoricalAwards=false
• 关键词: DDDAS; SMRP; Framework; Dynamic; Data

CMS-0540132, PI: Yu Ding, Texas A&M UniversityCMS-0540278, PI: Jiong Tang, University of ConnecticutAbstractThis collaborative research (0540132, PI: Yu Ding, Texas A&M University; and 0540278, PI: Jiong Tang, University of Connecticut) will provide a dynamic data-driven framework for wind turbine diagnosis. This new methodology is fundamentally different from the current practice whose performance is limited due to the non-dynamic and non-robust nature in the modeling approaches and in the data collection and processing strategies. This framework consists of two robust data pre-processing modules for highlighting fault features and removing sensor anomaly, three interrelated, multi-level models that describe different details of the system behaviors, and one dynamic strategy for the robust local interrogation that allows for measurements to be adaptively taken according to specific physical conditions and the associated risk level. It incorporates both historical data and on-line signals into the system modeling, and enables the ability to adaptively alter data collection procedures to best capture the critical system features. Collectively, these components lead to a robust and sensitive diagnosis system for wind turbines. This research is strengthened by a close collaboration with industry that will provide abundant historical sensor data and detailed system characterization, and also offer in-field implementation opportunities. The proposed research will have strategic importance on the utilization of wind energy that is currently the most viable clean energy alternative. Today, in the vast areas that have low wind speed, wind energy cannot compete with traditional energy sources as it has a higher cost, mainly owing to its high maintenance costs and low confidence in the diagnosis technology. This dynamic and data-driven fault diagnosis will play a key role in enabling a cost-effective generation of wind electricity. Progress in the fault diagnosis of blades and gearboxes will also benefit the power generation, automobile, aerospace, and engine industries. Meanwhile, the collaborative nature of this research will provide students with a multidisciplinary training and will bring industrial perspective to the universities. This project will have a long-term impact on education through the curriculum development and will promote the public awareness of clean energy concept through outreaches to high schools.

CMS-0540132, PI: Yu Ding, Texas A&M UniversityCMS-0540278, PI: Jiong Tang, University of ConnecticutAbstractThis collaborative research (0540132, PI: Yu Ding, Texas A&M University; and 0540278, PI: Jiong Tang, University of Connecticut) will provide a dynamic data-driven framework for wind涡轮诊断。 这种新方法与当前实践的根本不同，由于当前的实践，由于建模方法以及数据收集和处理策略中的非动态和非稳定性，其性能受到限制。 该框架由两个可靠的数据预处理模块组成，用于突出显示故障功能并删除传感器异常，三个相互关联的多级模型，描述了系统行为的不同细节，以及一个可靠的本地询问的动态策略，可以根据特定的物理条件和相关的风险水平适应测量值。 它将历史数据和在线信号同时纳入系统建模，并使能够自适应更改数据收集过程以最好地捕获关键系统功能。 总的来说，这些组件为风力涡轮机提供了强大而敏感的诊断系统。 与行业的密切合作可以加强这项研究，该行业将提供丰富的历史传感器数据和详细的系统表征，并提供场地实施机会。 拟议的研究将对目前最可行的清洁能源替代品的风能利用具有战略意义。 如今，在风速较低的广阔地区，风能无法与传统能源竞争，因为它具有较高的成本，这主要是由于其高维护成本和对诊断技术的信心较低。 这种动态和数据驱动的故障诊断将在实现具有成本效益的风能产生方面发挥关键作用。 叶片和变速箱故障诊断的进展也将使发电，汽车，航空航天和发动机行业受益。同时，这项研究的协作性质将为学生提供多学科的培训，并将为大学带来工业视角。 该项目将通过课程开发对教育产生长期影响，并将通过向高中推广来提高公众对清洁能源概念的认识。