RII Track-4: Adaptive Fault Detection and Diagnosis Based on Growing Gaussian Mixture Regressions for High-Performance HVAC Systems

RII Track-4：高性能 HVAC 系统基于增长高斯混合回归的自适应故障检测和诊断

基本信息

批准号：
1929209
负责人：
Liping Wang
金额：
$ 21.71万
依托单位：
University of Wyoming
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-02-01 至 2023-01-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1929209&HistoricalAwards=false
关键词：
RII Track Adaptive Fault Detection

项目摘要

Environmental impacts, as well as resource consumption, of building operations are significant throughout the entire life cycle of buildings. Heating ventilation and air conditioning (HVAC) systems consume about two-thirds of the total energy used in commercial buildings. Despite national efforts toward improving performance and sustainability, many existing HVAC systems in buildings do not run efficiently, due to equipment degradation, sensors being out of calibration, or improper control operations. Such problems can result in high maintenance costs, occupant discomfort, and wasted energy. Fault detection and diagnosis (FDD) for HVAC systems in buildings detect and identify operational faults based on the analysis of measured system behaviors. FDD technology is critical to improving building energy efficiency, and reducing or eliminating wasted energy in buildings caused by operational faults. The major challenge in current FDD technology is that the training data available to create diagnostic algorithms do not include all possible operating conditions that the testing systems experience throughout the life cycle. Given that the training data for FDDs does not cover all operating conditions, FDD algorithms for building HVAC systems must evolve along with the changes in building systems and components. The goal of this project is to enhance the robustness and efficiency of FDD technology for high-performance HVAC systems. The proposed research will lead to several broader impacts including research participation of underrepresented undergraduates, K-12 outreach activities, and sharing the experimental data and the FDD method for high-performance HVAC systems with other researchers. The knowledge gained from this research has the potential to significantly enhance building energy efficiency. The overall research goal is to advance robustness and efficiency of Fault detection and diagnosis (FDD) technology through an adaptive machine learning-based approach for high-performance Heating ventilation and air conditioning (HVAC) systems. This research closes critical knowledge gaps in the FDDs for high-performance HVAC systems. First, the experimental study on common faults in high-performance HVAC systems at the Center for High Performance Buildings, Purdue University will result in a thorough understanding of fault features, including system behaviors as well as impacts on energy consumption and environmental conditions. While extensive research has been conducted on the FDD for conventional HVAC systems, the FDD for high-performance HVAC systems has rarely been studied. The experimental data pertaining to common faults in high-performance HVAC systems that will be obtained as a part of this project will, thus, be an invaluable asset to the FDD research community. Second, this research will yield an adaptive FDD method based on growing Gaussian mixture regressions for high-performance HVAC systems in commercial buildings. Traditional FDD methods learn from training data tested under limited operating conditions, after which the learning stops. This new FDD method adapts to the changes in HVAC operating environments, evolves with the changes in building systems and components, and learns to diagnose new faulty conditions.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

在整个建筑物的整个生命周期中，环境影响以及资源消耗都很重要。加热通风和空调（HVAC）系统消耗了商业建筑中使用的总能量的三分之二。尽管全国性的努力提高了绩效和可持续性，但由于设备降解，传感器不受校准或不适当的控制操作，建筑物中许多现有的HVAC系统并未有效运行。此类问题可能导致高维护成本，居住者不适和浪费能量。建筑物中HVAC系统的故障检测和诊断（FDD）根据测量系统行为的分析检测和识别操作故障。 FDD技术对于提高建筑能源效率，减少或消除由操作故障引起的建筑物中的浪费能量至关重要。当前FDD技术的主要挑战是，可用于创建诊断算法的培训数据不包括测试系统在整个生命周期中经历的所有可能的操作条件。鉴于FDD的培训数据并不涵盖所有操作条件，因此，用于构建HVAC系统的FDD算法必须随着建筑系统和组件的变化而发展。该项目的目的是提高FDD技术对高性能HVAC系统的鲁棒性和效率。拟议的研究将导致一些更广泛的影响，包括代表性不足的本科生，K-12外展活动的研究参与，并与其他研究人员共享实验数据和实验数据和FDD方法。从这项研究中获得的知识有可能显着提高建筑能源效率。总体研究目标是通过基于自适应机器学习的高性能加热通风和空调（HVAC）系统来提高故障检测和诊断（FDD）技术的鲁棒性和效率。这项研究缩小了高性能HVAC系统的FDD中的关键知识差距。首先，关于高性能建筑中心的高性能HVAC系统中常见故障的实验研究，普渡大学将对故障特征进行彻底了解，包括系统行为以及对能耗和环境条件的影响。尽管已经针对常规HVAC系统进行了FDD进行了广泛的研究，但很少研究用于高性能HVAC系统的FDD。因此，与高性能HVAC系统中的常见故障有关的实验数据将作为该项目的一部分获得，因此对于FDD研究界来说将是一项无价的资产。其次，这项研究将产生一种自适应FDD方法，基于对商业建筑中高性能HVAC系统的高斯混合物回归的增长。传统的FDD方法从在有限的操作条件下测试的培训数据中学习，此后学习停止。这种新的FDD方法适应了HVAC操作环境的变化，随着建筑系统和组件的变化而演变，并学会了诊断新的故障状况。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的审查标准来通过评估来通过评估来支持的。