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

CMS-0540132，PI：Yu Ding，德克萨斯农工大学CMS-0540278，PI：Jiong Tang，康涅狄格大学摘要这项合作研究(0540132，PI：Yu Ding，德克萨斯农工大学；0540278，PI：Jiong Tang，康涅狄格大学)将为风力涡轮机诊断提供一个动态数据驱动的框架。这种新的方法与目前的做法有根本的不同，目前的做法由于建模方法以及数据收集和处理策略的非动态和非稳健性而受到限制。该框架包括两个用于突出故障特征和消除传感器异常的稳健数据预处理模块，三个相互关联的多级模型，用于描述系统行为的不同细节，以及一个用于稳健本地询问的动态策略，该策略允许根据特定的物理条件和相关的风险水平自适应地进行测量。它将历史数据和在线信号合并到系统建模中，并能够自适应地更改数据收集程序，以最好地捕获关键系统功能。总而言之，这些组件为风力涡轮机提供了一个强大而灵敏的诊断系统。这项研究通过与业界的密切合作得到加强，这将提供丰富的历史传感器数据和详细的系统表征，并提供现场实施机会。拟议的研究将对风能的利用具有战略意义，风能是目前最可行的清洁能源替代品。今天，在风速较低的广大地区，风能无法与传统能源竞争，因为它的成本更高，主要是因为它的维护成本高，对诊断技术的信心不高。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力发电方面发挥关键作用。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，这项研究的协作性将为学生提供多学科的培训，并将为大学带来产业视角。这个项目将通过课程开发对教育产生长期影响，并通过外展到高中提高公众对清洁能源概念的认识。

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.