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:
- 金额:$ 21.71万
- 依托单位:
- 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:
- 关键词:
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.
建筑作业对环境的影响以及对资源的消耗在建筑物的整个生命周期中都是重要的。供暖、通风和空调(HVAC)系统消耗的能源约占商业建筑总能耗的三分之二。尽管各国都在努力提高性能和可持续性,但由于设备退化、传感器未校准或控制操作不当,建筑物中许多现有的暖通空调系统运行效率不高。这样的问题可能会导致高昂的维护成本、乘员不适和能源浪费。建筑暖通空调系统的故障检测与诊断(FDD)是基于对测量到的系统行为的分析来检测和识别运行故障。FDD技术对于提高建筑能效,减少或消除建筑运行故障造成的能源浪费至关重要。当前FDD技术的主要挑战是,可用于创建诊断算法的训练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的训练数据不能涵盖所有运行条件,用于构建暖通空调系统的FDD算法必须随着建筑系统和部件的变化而发展。该项目的目标是提高FDD技术在高性能暖通空调系统中的稳健性和效率。拟议的研究将产生几个更广泛的影响,包括代表不足的本科生的研究参与,K-12外展活动,以及与其他研究人员共享高性能暖通空调系统的实验数据和FDD方法。从这项研究中获得的知识有可能显著提高建筑能效。总体研究目标是通过一种基于自适应机器学习的方法来提高故障检测和诊断(FDD)技术的健壮性和效率,用于高性能供暖、通风和空调(HVAC)系统。这项研究填补了高性能暖通空调系统FDD中的关键知识空白。首先,在普渡大学高性能建筑中心进行的高性能暖通空调系统常见故障的实验研究将有助于深入了解故障特征,包括系统行为以及对能耗和环境条件的影响。虽然人们对常规暖通空调系统的FDD进行了广泛的研究,但对高性能暖通空调系统的FDD的研究还很少。因此,作为该项目的一部分,将获得的有关高性能暖通空调系统常见故障的实验数据将成为FDD研究界的无价资产。其次,针对商业建筑的高性能暖通空调系统,本研究将提出一种基于增长高斯混合回归的自适应FDD方法。传统的FDD方法从有限运行条件下测试的训练数据中学习,然后停止学习。这种新的FDD方法适应暖通空调运行环境的变化,随着建筑系统和部件的变化而发展,并学习诊断新的故障状况。该奖项反映了NSF的法定使命,并通过使用基金会的智力优势和更广泛的影响审查标准进行评估,被认为值得支持。
项目成果
期刊论文数量(4)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
A Novel Hybrid Modeling Method for Predicting Energy Use of Hydronic Radiant Slab Systems
- DOI:
- 发表时间:2022
- 期刊:
- 影响因子:0
- 作者:Liping Wang;Lichen Wu;James Braun
- 通讯作者:Liping Wang;Lichen Wu;James Braun
Fault Detection and Diagnostic Method Based on Evolving Datadriven Model for Radiant Heating and Cooling Systems
基于演化数据驱动模型的辐射供暖和制冷系统故障检测与诊断方法
- DOI:
- 发表时间:2022
- 期刊:
- 影响因子:0
- 作者:Dahal, Sujit;Wang, Liping;Braun, James
- 通讯作者:Braun, James
An evolving learning method —growing Gaussian mixture regression—for modeling passive chilled beam systems in buildings
- DOI:10.1016/j.enbuild.2022.112227
- 发表时间:2022-05
- 期刊:
- 影响因子:6.7
- 作者:Liping Wang;James Braun;Sujit Dahal
- 通讯作者:Liping Wang;James Braun;Sujit Dahal
An evolving learning-based fault detection and diagnosis method: Case study for a passive chilled beam system
一种不断发展的基于学习的故障检测和诊断方法:被动冷梁系统案例研究
- DOI:10.1016/j.energy.2022.126337
- 发表时间:2023
- 期刊:
- 影响因子:9
- 作者:Wang, Liping;Braun, James;Dahal, Sujit
- 通讯作者:Dahal, Sujit
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
数据更新时间:{{ journalArticles.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ monograph.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ sciAawards.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ conferencePapers.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ patent.updateTime }}
Liping Wang其他文献
Tribocorrosion behaviors of multilayer PVD DLC coated 304L stainless steel in seawater
多层PVD DLC涂层304L不锈钢在海水中的摩擦腐蚀行为
- DOI:
10.1016/j.diamond.2017.09.002 - 发表时间:
2017-10 - 期刊:
- 影响因子:4.1
- 作者:
Yuwei Ye;Yongxin Wang;Xinli Ma;Dawei Zhang;Liping Wang;Xiaogang Li - 通讯作者:
Xiaogang Li
Modulation of Innate Defensive Responses by Locus Coeruleus-Superior Colliculus Circuit
蓝斑-上丘回路对先天防御反应的调节
- DOI:
10.1177/1179069518792035 - 发表时间:
2018-08 - 期刊:
- 影响因子:0
- 作者:
Lei Li;Liping Wang - 通讯作者:
Liping Wang
Thermal Percolation of Antiperovskite Superionic Conductor into Porous MXene Scaffold for High‐Capacity and Stable Lithium Metal Battery
反钙钛矿超离子导体热渗透到多孔 MXene 支架中用于高容量和稳定的锂金属电池
- DOI:
10.1002/smtd.202200980 - 发表时间:
2022-10 - 期刊:
- 影响因子:12.4
- 作者:
Yang Li;Long Kong;Haochen Yang;Shuai Li;Zhi Deng;Shuo Li;Liping Wang;Jim Yang Lee;Yusheng Zhao;Po‐Yen Chen - 通讯作者:
Po‐Yen Chen
Preparation of mercury ions absorbent from filter paper by surface sol-gel process and functionalized monolayers treatment
表面溶胶-凝胶法及功能化单分子膜处理滤纸制备汞离子吸收剂
- DOI:
10.1109/iswrep.2011.5893560 - 发表时间:
2011 - 期刊:
- 影响因子:0
- 作者:
Kunrong Lai;Wei Wang;Liping Wang - 通讯作者:
Liping Wang
SCID-hu Thy / Liv Mice : Evidence of Indirect Immature Thymocytes in HIV-1-Infected Induction of MHC Class I Expression on Kaneshima and Lishan
SCID-hu Thy / Liv 小鼠:HIV-1 感染的间接未成熟胸腺细胞诱导 Kaneshima 和 Lishan 上 MHC I 类表达的证据
- DOI:
- 发表时间:
1999 - 期刊:
- 影响因子:0
- 作者:
G. Kovalev;K. Duus;Liping Wang;R. Lee;M. Bonyhadi;D. Ho;J. McCune;H. Kaneshima;L. Su - 通讯作者:
L. Su
Liping Wang的其他文献
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
{{ truncateString('Liping Wang', 18)}}的其他基金
REU Site: Controlled Environment Agriculture (CEAfREU)
REU 站点:受控环境农业 (CEAfREU)
- 批准号:
2349765 - 财政年份:2024
- 资助金额:
$ 21.71万 - 项目类别:
Standard Grant
Collaborative Research: Electrically Modulated Near-field Thermophotonics with Metal-Oxide-Semiconductor Nanostructures
合作研究:金属氧化物半导体纳米结构的电调制近场热光子学
- 批准号:
2309663 - 财政年份:2023
- 资助金额:
$ 21.71万 - 项目类别:
Standard Grant
Tunable Super-Planckian Near-field Radiative Heat Transfer with Thermochromic Metamaterials
使用热致变色超材料的可调谐超普朗克近场辐射传热
- 批准号:
2212342 - 财政年份:2022
- 资助金额:
$ 21.71万 - 项目类别:
Standard Grant
CAREER: Commercial Building Indoor Greenery Systems' Effects on Thermal Environment and Occupant Comfort under Climate Change
职业:气候变化下商业建筑室内绿化系统对热环境和居住者舒适度的影响
- 批准号:
1944823 - 财政年份:2020
- 资助金额:
$ 21.71万 - 项目类别:
Continuing Grant
CAREER: Coherent Understanding of Magnetic Resonance in Controlling Radiative Transport from Far to Near Field
职业:对磁共振控制从远场到近场的辐射传输的连贯理解
- 批准号:
1454698 - 财政年份:2015
- 资助金额:
$ 21.71万 - 项目类别:
Standard Grant
STTR Phase I: A Gas-Solid Spouted Bed Bioreactor for Solid State Fermentation to Produce Enzymes and Biochemicals from Plant Biomass
STTR 第一阶段:气固喷动床生物反应器,用于固态发酵,从植物生物质中生产酶和生物化学品
- 批准号:
0611075 - 财政年份:2006
- 资助金额:
$ 21.71万 - 项目类别:
Standard Grant
相似海外基金
RII Track-4:NSF: HEAL: Heterogeneity-aware Efficient and Adaptive Learning at Clusters and Edges
RII Track-4:NSF:HEAL:集群和边缘的异质性感知高效自适应学习
- 批准号:
2327452 - 财政年份:2024
- 资助金额:
$ 21.71万 - 项目类别:
Standard Grant
RII Track-4: NSF: Scalable MPI with Adaptive Compression for GPU-based Computing Systems
RII Track-4:NSF:适用于基于 GPU 的计算系统的具有自适应压缩的可扩展 MPI
- 批准号:
2327266 - 财政年份:2024
- 资助金额:
$ 21.71万 - 项目类别:
Standard Grant
RII Track-2 FEC: An Interdisciplinary Program for Research, Education, and Outreach on Climate Change and Adaptive Resilience in the Yazoo - Mississippi Delta
RII Track-2 FEC:亚祖 - 密西西比三角洲气候变化和适应性恢复力研究、教育和推广的跨学科计划
- 批准号:
2316382 - 财政年份:2023
- 资助金额:
$ 21.71万 - 项目类别:
Cooperative Agreement
MRI: Track 2 Development of Astrophysics Enabled by High Order Advanced Keck Adaptive Optics (HAKA)
MRI:高阶高级 Keck 自适应光学 (HAKA) 推动天体物理学的第 2 轨发展
- 批准号:
2320038 - 财政年份:2023
- 资助金额:
$ 21.71万 - 项目类别:
Standard Grant
RII Track-1: Adaptive and Resilient Infrastructures driven by Social Equity (ARISE)
RII Track-1:社会公平驱动的适应性和弹性基础设施 (ARISE)
- 批准号:
2148878 - 财政年份:2022
- 资助金额:
$ 21.71万 - 项目类别:
Cooperative Agreement
ExpandQISE: Track 1: Reimagining Adaptive Quantum Algorithms
ExpandQISE:轨道 1:重新构想自适应量子算法
- 批准号:
2231328 - 财政年份:2022
- 资助金额:
$ 21.71万 - 项目类别:
Standard Grant
SCC-IRG Track 1: Enabling Smart Cities in Coastal Regions of Environmental and Industrial Change: Building Adaptive Capacity through Sociotechnical Networks on the Texas Gulf Coast
SCC-IRG 第 1 轨道:在环境和工业变化的沿海地区实现智慧城市:通过德克萨斯州墨西哥湾沿岸的社会技术网络建设适应能力
- 批准号:
2231557 - 财政年份:2022
- 资助金额:
$ 21.71万 - 项目类别:
Standard Grant
SCC-IRG Track 2: Scalable Modeling and Adaptive Real-time Trust-based Communication (SMARTc) System for Roadway Inundations in Flood-Prone Communities
SCC-IRG 第 2 轨:针对易受洪水影响的社区道路洪水的可扩展建模和自适应实时基于信任的通信 (SMARTc) 系统
- 批准号:
1951745 - 财政年份:2020
- 资助金额:
$ 21.71万 - 项目类别:
Standard Grant
IRES Track I: Exploring Adaptive Responses to Dynamic Island Environments
IRES 轨道 I:探索对动态岛屿环境的适应性响应
- 批准号:
2025704 - 财政年份:2020
- 资助金额:
$ 21.71万 - 项目类别:
Standard Grant
Artificial Intelligence based adaptive and interpretable models for analyzing multi-track epigenomic sequential data
基于人工智能的自适应和可解释模型,用于分析多轨表观基因组序列数据
- 批准号:
437034 - 财政年份:2020
- 资助金额:
$ 21.71万 - 项目类别:
Operating Grants