CyberSEES: Type 2: Fault Detection, Diagnosis and Prognosis of HVAC systems

Cyber​​SEES：类型 2：HVAC 系统的故障检测、诊断和预测

• 批准号: 1331850
• 负责人: Krishna Pattipati
• 金额: $ 109万
• 依托单位: University of Connecticut
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-10-01 至 2017-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1331850&HistoricalAwards=false
• 关键词: CyberSEES; Type; Fault; Detection; Diagnosis

The goal of this multi-disciplinary project is to develop a simple, robust, generic, and scalable model-based and data-driven Fault Detection, Diagnosis and Prognosis (FDDP) process and the associated detection, inference and predictive analytics that are applicable to a variety of buildings. The research is motivated by the observation that buildings account for more than 40% of US energy consumption. Heating, Ventilation and Air Conditioning (HVAC) constitutes 57% of energy used in commercial and residential buildings, valued at $223B in 2009. About 20% of the energy consumed by HVAC is wasted due to abrupt faults (e.g., stuck dampers), performance degradations (e.g., air filter clogging), poor controls (e.g., biases in set points), and improper commissioning (e.g., poorly balanced parallel chillers). This project will develop FDDP methodologies for HVACs to improve equipment availability, lower energy and operating costs, extend equipment life, and enhance occupants' comfort. The FDDP process will be validated and evaluated by applying it to UConn's Tech Park Building; Duncaster, a life-care retirement community, located in Bloomfield, CT; and potentially to others. The project contributes to the vision of green and sustainable buildings equipped with cyber-physical substrata consisting of HVAC modules, networked sensors providing information on spatial and temporal distribution of occupants, smart building management systems providing situation awareness and decision support to human operators, and improved tenant comfort.

这个多学科项目的目标是开发一个简单，强大，通用和可扩展的基于模型和数据驱动的故障检测，诊断和预测（FDDP）过程以及适用于各种建筑物的相关检测，推理和预测分析。 这项研究的动机是观察到建筑物占美国能源消耗的40%以上。供暖、通风和空调（HVAC）占商业和住宅建筑使用能源的57%，2009年价值2230亿美元。HVAC消耗的能量中约有20%是由于突发故障（例如，阻尼器卡住），性能下降（例如，空气过滤器堵塞），不良控制（例如，设定点的偏差），以及不适当的调试（例如，平衡差的平行冷却器）。该项目将为暖通空调开发FDDP方法，以提高设备可用性，降低能源和运营成本，延长设备寿命，并提高居住者的舒适度。 FDDP过程将通过将其应用于康州大学的科技园大楼、位于康涅狄格州布卢姆菲尔德的生活护理退休社区邓卡斯特以及其他潜在的社区来进行验证和评估。 该项目有助于实现绿色和可持续建筑的愿景，这些建筑配备了由HVAC模块组成的网络物理基础，提供有关居住者空间和时间分布信息的网络传感器，为人类操作员提供情况感知和决策支持的智能建筑管理系统，以及改善租户舒适度。