A review of computing-based automated fault detection and diagnosis of heating, ventilation and air conditioning systems

Chen, Jianli; Zhang, Liang; Li, Yanfei; Shi, Yifu; Gao, Xinghua; Hu, Yuqing

doi:10.1016/j.rser.2022.112395

A review of computing-based automated fault detection and diagnosis of heating, ventilation and air conditioning systems

Journal Article · Wed Apr 06 00:00:00 EDT 2022 · Renewable and Sustainable Energy Reviews

DOI:https://doi.org/10.1016/j.rser.2022.112395· OSTI ID:1865322

^[1]; ^[2]; Li, Yanfei ^[3]; Shi, Yifu ^[4]; ^[5]; Hu, Yuqing ^[6]

University of Utah, Salt Lake City, UT (United States)
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Georgia Institute of Technology, Atlanta, GA (United States)
Virginia Polytechnic Institute and State Univ. (Virginia Tech), Blacksburg, VA (United States)
Pennsylvania State University, University Park, PA (United States)

We report faults in Heating, Ventilation, and Air Conditioning (HVAC) systems of buildings result in significant energy waste in building operation. With fast-growing sensing data availability and advancement in computing, computational modeling has demonstrated strong capability to detect and diagnose HVAC system faults, hence, ensuring efficient building operation. This paper comprehensively reviews the state-of-the-art computing-based fault detection and diagnosis (FDD) for HVAC systems. Overall, the reviewed computing-based FDD methods are classified as two major approaches: knowledge-based and data-driven approaches. We then identify multiple important topics, including data availability, training data size, data quality, approach generality, capability, interpretability, and required modeling efforts, along with corresponding metrics to summarize the most updated FDD development. Generally, the knowledge-based approaches are further divided as physics-based modeling, Diagnostic Bayesian Network, and performance indicator-based methods while data-driven approaches include supervised learning, unsupervised learning, and regression and statistics-based methods. State-of-the-art FDD development, remaining challenges, and future research directions are further discussed to push forward FDD in practice. Availability of fault data, capability of existing methods to deal with complex fault situations (such as simultaneous faults), modeling interpretability for data-driven methods, and required engineering efforts for physics-based methods are identified as remaining challenges in FDD development. Improving modeling fidelity and reducing modeling efforts are essential for applying physics-based methods in real buildings. Meanwhile, addressing fault data availability, increasing algorithm adaptability, and handling multiple faults are essential to further enhance the applicability of data-driven FDD approaches.

View Accepted Manuscript (DOE)

Research Organization:: National Renewable Energy Laboratory (NREL), Golden, CO (United States); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE; USDOE Office of Energy Efficiency and Renewable Energy (EERE)

Grant/Contract Number:: AC05-00OR22725; AC36-08GO28308

OSTI ID:: 1865322

Alternate ID(s):: OSTI ID: 1888914

Report Number(s):: NREL/JA-5500-82739; MainId:83512; UUID:a0dc707b-8b7f-4d66-898d-1dec6e9f1260; MainAdminID:64393

Journal Information:: Renewable and Sustainable Energy Reviews, Journal Name: Renewable and Sustainable Energy Reviews Vol. 161; ISSN 1364-0321

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

References (175)

A critical review of fault modeling of HVAC systems in buildings Li, Yanfei; O’Neill, Zheng Building Simulation, Vol. 11, Issue 5 https://doi.org/10.1007/s12273-018-0458-4	journal	July 2018
Principal component analysis Wold, Svante; Esbensen, Kim; Geladi, Paul Chemometrics and Intelligent Laboratory Systems, Vol. 2, Issue 1-3 https://doi.org/10.1016/0169-7439(87)80084-9	journal	August 1987
Creating an automated chiller fault detection and diagnostics tool using a data fault library Bailey, Margaret B.; Kreider, Jan F. ISA Transactions, Vol. 42, Issue 3 https://doi.org/10.1016/S0019-0578(07)60149-9	journal	July 2003
Development of parameter based fault detection and diagnosis technique for energy efficient building management system Kumar, Sanjay; Sinha, S.; Kojima, T. Energy Conversion and Management, Vol. 42, Issue 7 https://doi.org/10.1016/S0196-8904(00)00106-0	journal	May 2001
ARX and AFMM model-based on-line real-time data base diagnosis of sudden fault in AHU of VAV system Yoshida, Harunori; Kumar, Sanjay Energy Conversion and Management, Vol. 40, Issue 11 https://doi.org/10.1016/S0196-8904(99)00022-9	journal	July 1999
Subsystem level fault diagnosis of a building's air-handling unit using general regression neural networks Lee, Won-Yong; House, John M.; Kyong, Nam-Ho Applied Energy, Vol. 77, Issue 2 https://doi.org/10.1016/S0306-2619(03)00107-7	journal	February 2004
Online fault detection and diagnosis in VAV air handling unit by RARX modeling Yoshida, Harunori; Kumar, Sanjay; Morita, Yasunori Energy and Buildings, Vol. 33, Issue 4 https://doi.org/10.1016/S0378-7788(00)00121-3	journal	April 2001
Fault detection in HVAC systems using model-based feedforward control Salsbury, T. I.; Diamond, R. C. Energy and Buildings, Vol. 33, Issue 4 https://doi.org/10.1016/S0378-7788(00)00122-5	journal	April 2001
Fault detection for non-condensing boilers using simulated building automation system sensor data Shohet, Rony; Kandil, Mohamed S.; Wang, Yidan Advanced Engineering Informatics, Vol. 46 https://doi.org/10.1016/j.aei.2020.101176	journal	October 2020
Sensor-fault detection, diagnosis and estimation for centrifugal chiller systems using principal-component analysis method Wang, Shengwei; Cui, Jingtan Applied Energy, Vol. 82, Issue 3 https://doi.org/10.1016/j.apenergy.2004.11.002	journal	November 2005
Fault diagnosis for temperature, flow rate and pressure sensors in VAV systems using wavelet neural network Du, Zhimin; Jin, Xinqiao; Yang, Yunyu Applied Energy, Vol. 86, Issue 9 https://doi.org/10.1016/j.apenergy.2009.01.015	journal	September 2009
Advances in heat pump systems: A review Chua, K. J.; Chou, S. K.; Yang, W. M. Applied Energy, Vol. 87, Issue 12 https://doi.org/10.1016/j.apenergy.2010.06.014	journal	December 2010
Application of machine learning in the fault diagnostics of air handling units Najafi, Massieh; Auslander, David M.; Bartlett, Peter L. Applied Energy, Vol. 96 https://doi.org/10.1016/j.apenergy.2012.02.049	journal	August 2012
Pattern recognition-based chillers fault detection method using Support Vector Data Description (SVDD) Zhao, Yang; Wang, Shengwei; Xiao, Fu Applied Energy, Vol. 112 https://doi.org/10.1016/j.apenergy.2012.12.043	journal	December 2013
Multi-source information fusion based fault diagnosis of ground-source heat pump using Bayesian network Cai, Baoping; Liu, Yonghong; Fan, Qian Applied Energy, Vol. 114 https://doi.org/10.1016/j.apenergy.2013.09.043	journal	February 2014
Robust on-line fault detection diagnosis for HVAC components based on nonlinear state estimation techniques Bonvini, Marco; Sohn, Michael D.; Granderson, Jessica Applied Energy, Vol. 124 https://doi.org/10.1016/j.apenergy.2014.03.009	journal	July 2014
A system-level fault detection and diagnosis method for low delta-T syndrome in the complex HVAC systems Gao, Dian-ce; Wang, Shengwei; Shan, Kui Applied Energy, Vol. 164 https://doi.org/10.1016/j.apenergy.2015.02.025	journal	February 2016
Fault detection and diagnosis of chillers using Bayesian network merged distance rejection and multi-source non-sensor information Wang, Zhanwei; Wang, Zhiwei; He, Suowei Applied Energy, Vol. 188 https://doi.org/10.1016/j.apenergy.2016.11.130	journal	February 2017
Deep learning-based fault diagnosis of variable refrigerant flow air-conditioning system for building energy saving Guo, Yabin; Tan, Zehan; Chen, Huanxin Applied Energy, Vol. 225 https://doi.org/10.1016/j.apenergy.2018.05.075	journal	September 2018
Design of machine learning models with domain experts for automated sensor selection for energy fault detection Hu, R. L.; Granderson, J.; Auslander, D. M. Applied Energy, Vol. 235 https://doi.org/10.1016/j.apenergy.2018.10.107	journal	February 2019
A review of machine learning in building load prediction Zhang, Liang; Wen, Jin; Li, Yanfei Applied Energy, Vol. 285 https://doi.org/10.1016/j.apenergy.2021.116452	journal	March 2021
Enhanced chiller sensor fault detection, diagnosis and estimation using wavelet analysis and principal component analysis methods Xu, Xinhua; Xiao, Fu; Wang, Shengwei Applied Thermal Engineering, Vol. 28, Issue 2-3 https://doi.org/10.1016/j.applthermaleng.2007.03.021	journal	February 2008
An isolation enhanced PCA method with expert-based multivariate decoupling for sensor FDD in air-conditioning systems Xiao, Fu; Wang, Shengwei; Xu, Xinhua Applied Thermal Engineering, Vol. 29, Issue 4 https://doi.org/10.1016/j.applthermaleng.2008.03.046	journal	March 2009
Study on a hybrid SVM model for chiller FDD applications Han, H.; Gu, B.; Kang, J. Applied Thermal Engineering, Vol. 31, Issue 4 https://doi.org/10.1016/j.applthermaleng.2010.10.021	journal	March 2011
A statistical fault detection and diagnosis method for centrifugal chillers based on exponentially-weighted moving average control charts and support vector regression Zhao, Yang; Wang, Shengwei; Xiao, Fu Applied Thermal Engineering, Vol. 51, Issue 1-2 https://doi.org/10.1016/j.applthermaleng.2012.09.030	journal	March 2013
Diagnostic Bayesian networks for diagnosing air handling units faults – Part II: Faults in coils and sensors Zhao, Yang; Wen, Jin; Wang, Shengwei Applied Thermal Engineering, Vol. 90 https://doi.org/10.1016/j.applthermaleng.2015.07.001	journal	November 2015
Diagnostic Bayesian networks for diagnosing air handling units faults – part I: Faults in dampers, fans, filters and sensors Zhao, Yang; Wen, Jin; Xiao, Fu Applied Thermal Engineering, Vol. 111 https://doi.org/10.1016/j.applthermaleng.2015.09.121	journal	January 2017
A refrigerant charge fault detection method for variable refrigerant flow (VRF) air-conditioning systems Liu, Jiangyan; Hu, Yunpeng; Chen, Huanxin Applied Thermal Engineering, Vol. 107 https://doi.org/10.1016/j.applthermaleng.2016.03.147	journal	August 2016
Fault detection and diagnosis of chiller using Bayesian network classifier with probabilistic boundary He, Suowei; Wang, Zhiwei; Wang, Zhanwei Applied Thermal Engineering, Vol. 107 https://doi.org/10.1016/j.applthermaleng.2016.06.153	journal	August 2016
A novel efficient SVM-based fault diagnosis method for multi-split air conditioning system’s refrigerant charge fault amount Sun, Kaizheng; Li, Guannan; Chen, Huanxin Applied Thermal Engineering, Vol. 108 https://doi.org/10.1016/j.applthermaleng.2016.07.109	journal	September 2016
Refrigerant charge fault diagnosis in the VRF system using Bayesian artificial neural network combined with ReliefF filter Shi, Shubiao; Li, Guannan; Chen, Huanxin Applied Thermal Engineering, Vol. 112 https://doi.org/10.1016/j.applthermaleng.2016.10.043	journal	February 2017
Liquid floodback detection for scroll compressor in a VRF system under heating mode Wang, Jiangyu; Li, Guannan; Chen, Huanxin Applied Thermal Engineering, Vol. 114 https://doi.org/10.1016/j.applthermaleng.2016.11.200	journal	March 2017
Modularized PCA method combined with expert-based multivariate decoupling for FDD in VRF systems including indoor unit faults Guo, Yabin; Li, Guannan; Chen, Huanxin Applied Thermal Engineering, Vol. 115 https://doi.org/10.1016/j.applthermaleng.2017.01.008	journal	March 2017
Optimized neural network-based fault diagnosis strategy for VRF system in heating mode using data mining Guo, Yabin; Li, Guannan; Chen, Huanxin Applied Thermal Engineering, Vol. 125 https://doi.org/10.1016/j.applthermaleng.2017.07.065	journal	October 2017
A hybrid ICA-BPNN-based FDD strategy for refrigerant charge faults in variable refrigerant flow system Sun, Shaobo; Li, Guannan; Chen, Huanxin Applied Thermal Engineering, Vol. 127 https://doi.org/10.1016/j.applthermaleng.2017.08.047	journal	December 2017
An efficient VRF system fault diagnosis strategy for refrigerant charge amount based on PCA and dual neural network model Shi, Shubiao; Li, Guannan; Chen, Huanxin Applied Thermal Engineering, Vol. 129 https://doi.org/10.1016/j.applthermaleng.2017.09.117	journal	January 2018
An improved decision tree-based fault diagnosis method for practical variable refrigerant flow system using virtual sensor-based fault indicators Li, Guannan; Chen, Huanxin; Hu, Yunpeng Applied Thermal Engineering, Vol. 129 https://doi.org/10.1016/j.applthermaleng.2017.10.013	journal	January 2018
Feature selection based on Bayesian network for chiller fault diagnosis from the perspective of field applications Wang, Zhanwei; Wang, Zhiwei; Gu, Xiaowei Applied Thermal Engineering, Vol. 129 https://doi.org/10.1016/j.applthermaleng.2017.10.079	journal	January 2018
Enhanced chiller fault detection using Bayesian network and principal component analysis Wang, Zhanwei; Wang, Lin; Liang, Kunfeng Applied Thermal Engineering, Vol. 141 https://doi.org/10.1016/j.applthermaleng.2018.06.037	journal	August 2018
Chiller sensor fault detection based on empirical mode decomposition threshold denoising and principal component analysis Mao, Qianjun; Fang, Xi; Hu, Yunpeng Applied Thermal Engineering, Vol. 144 https://doi.org/10.1016/j.applthermaleng.2018.07.104	journal	November 2018
Least squares support vector machine (LS-SVM)-based chiller fault diagnosis using fault indicative features Han, Hua; Cui, Xiaoyu; Fan, Yuqiang Applied Thermal Engineering, Vol. 154 https://doi.org/10.1016/j.applthermaleng.2019.03.111	journal	May 2019
Chiller fault diagnosis with field sensors using the technology of imbalanced data Fan, Yuqiang; Cui, Xiaoyu; Han, Hua Applied Thermal Engineering, Vol. 159 https://doi.org/10.1016/j.applthermaleng.2019.113933	journal	August 2019
Sensor fault detection and diagnosis for a water source heat pump air-conditioning system based on PCA and preprocessed by combined clustering Zhang, Hongtao; Chen, Huanxin; Guo, Yabin Applied Thermal Engineering, Vol. 160 https://doi.org/10.1016/j.applthermaleng.2019.114098	journal	September 2019
Novel application of multi-model ensemble learning for fault diagnosis in refrigeration systems Zhang, Zhan; Han, Hua; Cui, Xiaoyu Applied Thermal Engineering, Vol. 164 https://doi.org/10.1016/j.applthermaleng.2019.114516	journal	January 2020
Fault detection based on Bayesian network and missing data imputation for building energy systems Wang, Zhanwei; Wang, Lin; Tan, Yingying Applied Thermal Engineering, Vol. 182 https://doi.org/10.1016/j.applthermaleng.2020.116051	journal	January 2021
Fault detection and diagnostics analysis of air conditioners using virtual sensors Kim, Woohyun; Lee, Je-Hyeon Applied Thermal Engineering, Vol. 191 https://doi.org/10.1016/j.applthermaleng.2021.116848	journal	June 2021
Unsupervised feature selection using swarm intelligence and consensus clustering for automatic fault detection and diagnosis in Heating Ventilation and Air Conditioning systems Yuwono, Mitchell; Guo, Ying; Wall, Josh Applied Soft Computing, Vol. 34 https://doi.org/10.1016/j.asoc.2015.05.030	journal	September 2015
A diagnostic tool for online sensor health monitoring in air-conditioning systems Xiao, Fu; Wang, Shengwei; Zhang, Jianping Automation in Construction, Vol. 15, Issue 4 https://doi.org/10.1016/j.autcon.2005.06.001	journal	July 2006
An online fault diagnosis tool of VAV terminals for building management and control systems Wang, Haitao; Chen, Youming; Chan, Cary W. H. Automation in Construction, Vol. 22 https://doi.org/10.1016/j.autcon.2011.06.018	journal	March 2012
Bayesian network based FDD strategy for variable air volume terminals Xiao, Fu; Zhao, Yang; Wen, Jin Automation in Construction, Vol. 41 https://doi.org/10.1016/j.autcon.2013.10.019	journal	May 2014
Application of pattern matching method for detecting faults in air handling unit system Li, Shun; Wen, Jin Automation in Construction, Vol. 43 https://doi.org/10.1016/j.autcon.2014.03.002	journal	July 2014
A BIM-enabled information infrastructure for building energy Fault Detection and Diagnostics Dong, Bing; O'Neill, Zheng; Li, Zhengwei Automation in Construction, Vol. 44 https://doi.org/10.1016/j.autcon.2014.04.007	journal	August 2014
A framework for knowledge discovery in massive building automation data and its application in building diagnostics Fan, Cheng; Xiao, Fu; Yan, Chengchu Automation in Construction, Vol. 50 https://doi.org/10.1016/j.autcon.2014.12.006	journal	February 2015
A sensor fault detection strategy for air handling units using cluster analysis Yan, Rui; Ma, Zhenjun; Kokogiannakis, Georgios Automation in Construction, Vol. 70 https://doi.org/10.1016/j.autcon.2016.06.005	journal	October 2016
An auto-deployed model-based fault detection and diagnosis approach for Air Handling Units using BIM and Modelica Andriamamonjy, Ando; Saelens, Dirk; Klein, Ralf Automation in Construction, Vol. 96 https://doi.org/10.1016/j.autcon.2018.09.016	journal	December 2018
Development and implementation of automated fault detection and diagnostics for building systems: A review Shi, Zixiao; O'Brien, William Automation in Construction, Vol. 104 https://doi.org/10.1016/j.autcon.2019.04.002	journal	August 2019
Fault detection and diagnosis based on improved PCA with JAA method in VAV systems Du, Zhimin; Jin, Xinqiao; Wu, Lizhou Building and Environment, Vol. 42, Issue 9 https://doi.org/10.1016/j.buildenv.2006.08.011	journal	September 2007
A hybrid FDD strategy for local system of AHU based on artificial neural network and wavelet analysis Fan, Bo; Du, Zhimin; Jin, Xinqiao Building and Environment, Vol. 45, Issue 12 https://doi.org/10.1016/j.buildenv.2010.05.031	journal	December 2010
A novel model-based fault detection method for temperature sensor using fractal correlation dimension Yang, Xue-Bin; Jin, Xin-Qiao; Du, Zhi-Min Building and Environment, Vol. 46, Issue 4 https://doi.org/10.1016/j.buildenv.2010.10.030	journal	April 2011
A virtual supply airflow rate meter for rooftop air-conditioning units Yu, Daihong; Li, Haorong; Yang, Mo Building and Environment, Vol. 46, Issue 6 https://doi.org/10.1016/j.buildenv.2010.12.017	journal	June 2011
Cross-level fault detection and diagnosis of building HVAC systems Wu, Siyu; Sun, Jian-Qiao Building and Environment, Vol. 46, Issue 8 https://doi.org/10.1016/j.buildenv.2011.01.017	journal	August 2011
A framework for simulation-based real-time whole building performance assessment Pang, Xiufeng; Wetter, Michael; Bhattacharya, Prajesh Building and Environment, Vol. 54 https://doi.org/10.1016/j.buildenv.2012.02.003	journal	August 2012
A method to compare simulated and measured data to assess building energy performance Maile, Tobias; Bazjanac, Vladimir; Fischer, Martin Building and Environment, Vol. 56 https://doi.org/10.1016/j.buildenv.2012.03.012	journal	October 2012
Fault detection and diagnosis for buildings and HVAC systems using combined neural networks and subtractive clustering analysis Du, Zhimin; Fan, Bo; Jin, Xinqiao Building and Environment, Vol. 73 https://doi.org/10.1016/j.buildenv.2013.11.021	journal	March 2014
Deep-learning-based fault detection and diagnosis of air-handling units Lee, Kuei-Peng; Wu, Bo-Huei; Peng, Shi-Lin Building and Environment, Vol. 157 https://doi.org/10.1016/j.buildenv.2019.04.029	journal	June 2019
A stair-step probabilistic approach for automatic anomaly detection in building ventilation system operation Alexandersen, Emil Kjøller; Skydt, Mathis Riber; Engelsgaard, Sebastian Skals Building and Environment, Vol. 157 https://doi.org/10.1016/j.buildenv.2019.04.036	journal	June 2019
Fault diagnosis based operation risk evaluation for air conditioning systems in data centers Zhu, Xu; Du, Zhimin; Jin, Xinqiao Building and Environment, Vol. 163 https://doi.org/10.1016/j.buildenv.2019.106319	journal	October 2019
Fault detection and diagnosis for indoor air quality in DCV systems: Application of 4S3F method and effects of DBN probabilities Taal, Arie; Itard, Laure Building and Environment, Vol. 174 https://doi.org/10.1016/j.buildenv.2019.106632	journal	May 2020
A systematic feature extraction and selection framework for data-driven whole-building automated fault detection and diagnostics in commercial buildings Zhang, Liang; Frank, Stephen; Kim, Janghyun Building and Environment, Vol. 186 https://doi.org/10.1016/j.buildenv.2020.107338	journal	December 2020
Modeling and fault diagnosis design for HVAC systems using recurrent neural networks Shahnazari, Hadi; Mhaskar, Prashant; House, John M. Computers & Chemical Engineering, Vol. 126 https://doi.org/10.1016/j.compchemeng.2019.04.011	journal	July 2019
A hybrid modeling approach integrating first-principles knowledge with statistical methods for fault detection in HVAC systems Hassanpour, Hesam; Mhaskar, Prashant; House, John M. Computers & Chemical Engineering, Vol. 142 https://doi.org/10.1016/j.compchemeng.2020.107022	journal	November 2020
Fault diagnosis for heat pumps with parameter identification and clustering Zogg, D.; Shafai, E.; Geering, H. P. Control Engineering Practice, Vol. 14, Issue 12 https://doi.org/10.1016/j.conengprac.2005.11.002	journal	December 2006
Data-driven Fault Detection and Diagnosis for HVAC water chillers Beghi, A.; Brignoli, R.; Cecchinato, L. Control Engineering Practice, Vol. 53 https://doi.org/10.1016/j.conengprac.2016.04.018	journal	August 2016
Application of the heat pipe to enhance the performance of the vapor compression refrigeration system Nakkaew, Santiphap; Chitipalungsri, Thunyawat; Ahn, Ho Seon Case Studies in Thermal Engineering, Vol. 15 https://doi.org/10.1016/j.csite.2019.100531	journal	November 2019
AHU sensor fault diagnosis using principal component analysis method Wang, Shengwei; Xiao, Fu Energy and Buildings, Vol. 36, Issue 2 https://doi.org/10.1016/j.enbuild.2003.10.002	journal	February 2004
Valve fault detection and diagnosis based on CMAC neural networks Wang, Shengwei; Jiang, Zhiming Energy and Buildings, Vol. 36, Issue 6 https://doi.org/10.1016/j.enbuild.2004.01.037	journal	June 2004
A fault detection and diagnosis strategy of VAV air-conditioning systems for improved energy and control performances Qin, Jianying; Wang, Shengwei Energy and Buildings, Vol. 37, Issue 10 https://doi.org/10.1016/j.enbuild.2004.12.011	journal	October 2005
Detection and diagnosis for multiple faults in VAV systems Du, Zhimin; Jin, Xinqiao Energy and Buildings, Vol. 39, Issue 8 https://doi.org/10.1016/j.enbuild.2006.09.015	journal	August 2007
A robot fault diagnostic tool for flow rate sensors in air dampers and VAV terminals Du, Zhimin; Jin, Xinqiao; Yang, Xuebin Energy and Buildings, Vol. 41, Issue 3 https://doi.org/10.1016/j.enbuild.2008.09.007	journal	March 2009
A fault detection technique for air-source heat pump water chiller/heaters Chen, Youming; Lan, Lili Energy and Buildings, Vol. 41, Issue 8 https://doi.org/10.1016/j.enbuild.2009.03.007	journal	August 2009
A system-level fault detection and diagnosis strategy for HVAC systems involving sensor faults Wang, Shengwei; Zhou, Qiang; Xiao, Fu Energy and Buildings, Vol. 42, Issue 4 https://doi.org/10.1016/j.enbuild.2009.10.017	journal	April 2010
Variable refrigerant flow systems: A review Aynur, Tolga N. Energy and Buildings, Vol. 42, Issue 7 https://doi.org/10.1016/j.enbuild.2010.01.024	journal	July 2010
A robust fault detection and diagnosis strategy for pressure-independent VAV terminals of real office buildings Wang, Haitao; Chen, Youming; Chan, Cary W. H. Energy and Buildings, Vol. 43, Issue 7 https://doi.org/10.1016/j.enbuild.2011.03.018	journal	July 2011
A top-down strategy with temporal and spatial partition for fault detection and diagnosis of building HVAC systems Wu, Siyu; Sun, J. Q. Energy and Buildings, Vol. 43, Issue 9 https://doi.org/10.1016/j.enbuild.2011.04.020	journal	September 2011
Fault diagnosis for sensors in air handling unit based on neural network pre-processed by wavelet and fractal Zhu, Yonghua; Jin, Xinqiao; Du, Zhimin Energy and Buildings, Vol. 44 https://doi.org/10.1016/j.enbuild.2011.09.043	journal	January 2012
Chiller sensor fault detection using a self-Adaptive Principal Component Analysis method Hu, Yunpeng; Chen, Huanxin; Xie, Junlong Energy and Buildings, Vol. 54 https://doi.org/10.1016/j.enbuild.2012.07.014	journal	November 2012
A rule augmented statistical method for air-conditioning system fault detection and diagnostics Li, Zhengwei; Paredis, Christiaan J. J.; Augenbroe, Godfried Energy and Buildings, Vol. 54 https://doi.org/10.1016/j.enbuild.2012.08.001	journal	November 2012
Online model-based fault detection and diagnosis strategy for VAV air handling units Wang, Haitao; Chen, Youming; Chan, Cary W. H. Energy and Buildings, Vol. 55 https://doi.org/10.1016/j.enbuild.2012.08.016	journal	December 2012
Development and testing of an Automated Building Commissioning Analysis Tool (ABCAT) Bynum, John D.; Claridge, David E.; Curtin, Jonathan M. Energy and Buildings, Vol. 55 https://doi.org/10.1016/j.enbuild.2012.08.038	journal	December 2012
A hybrid model-based fault detection strategy for air handling unit sensors Yang, Xue-Bin; Jin, Xin-Qiao; Du, Zhi-Min Energy and Buildings, Vol. 57 https://doi.org/10.1016/j.enbuild.2012.10.048	journal	February 2013
An intelligent chiller fault detection and diagnosis methodology using Bayesian belief network Zhao, Yang; Xiao, Fu; Wang, Shengwei Energy and Buildings, Vol. 57 https://doi.org/10.1016/j.enbuild.2012.11.007	journal	February 2013
Development of an RDP neural network for building energy consumption fault detection and diagnosis Magoulès, Frédéric; Zhao, Hai-xiang; Elizondo, David Energy and Buildings, Vol. 62 https://doi.org/10.1016/j.enbuild.2013.02.050	journal	July 2013
A model-based fault detection and diagnostic methodology based on PCA method and wavelet transform Li, Shun; Wen, Jin Energy and Buildings, Vol. 68 https://doi.org/10.1016/j.enbuild.2013.08.044	journal	January 2014
Sensor fault detection and its efficiency analysis in air handling unit using the combined neural networks Du, Zhimin; Fan, Bo; Chi, Jinlei Energy and Buildings, Vol. 72 https://doi.org/10.1016/j.enbuild.2013.12.038	journal	April 2014
Field implementation and evaluation of a decoupling-based fault detection and diagnostic method for chillers Zhao, Xinzhi; Yang, Mo; Li, Haorong Energy and Buildings, Vol. 72 https://doi.org/10.1016/j.enbuild.2014.01.003	journal	April 2014
ARX model based fault detection and diagnosis for chillers using support vector machines Yan, Ke; Shen, Wen; Mulumba, Timothy Energy and Buildings, Vol. 81 https://doi.org/10.1016/j.enbuild.2014.05.049	journal	October 2014
A review of fault detection and diagnosis methodologies on air-handling units Yu, Yuebin; Woradechjumroen, Denchai; Yu, Daihong Energy and Buildings, Vol. 82 https://doi.org/10.1016/j.enbuild.2014.06.042	journal	October 2014
Robust model-based fault diagnosis for air handling units Mulumba, Timothy; Afshari, Afshin; Yan, Ke Energy and Buildings, Vol. 86 https://doi.org/10.1016/j.enbuild.2014.10.069	journal	January 2015
Using a hybrid method to construct a computational efficient cooling coil model for an automated single-duct variable air volume system fault detection and diagnosis Song, Li; Wang, Gang Energy and Buildings, Vol. 92 https://doi.org/10.1016/j.enbuild.2015.01.029	journal	April 2015
A temperature-based approach to detect abnormal building energy consumption Lin, Guanjing; Claridge, David E. Energy and Buildings, Vol. 93 https://doi.org/10.1016/j.enbuild.2015.02.013	journal	April 2015
A combined passive-active sensor fault detection and isolation approach for air handling units Padilla, Miguel; Choinière, Daniel Energy and Buildings, Vol. 99 https://doi.org/10.1016/j.enbuild.2015.04.035	journal	July 2015
A robust online fault detection and diagnosis strategy of centrifugal chiller systems for building energy efficiency Tran, Dinh Anh Tuan; Chen, Youming; Chau, Minh Quang Energy and Buildings, Vol. 108 https://doi.org/10.1016/j.enbuild.2015.09.044	journal	December 2015
A statistical training data cleaning strategy for the PCA-based chiller sensor fault detection, diagnosis and data reconstruction method Hu, Yunpeng; Chen, Huanxin; Li, Guannan Energy and Buildings, Vol. 112 https://doi.org/10.1016/j.enbuild.2015.11.066	journal	January 2016
An improved fault detection method for incipient centrifugal chiller faults using the PCA-R-SVDD algorithm Li, Guannan; Hu, Yunpeng; Chen, Huanxin Energy and Buildings, Vol. 116 https://doi.org/10.1016/j.enbuild.2015.12.045	journal	March 2016
A robust fault detection and diagnosis strategy for multiple faults of VAV air handling units Wang, Haitao; Chen, Youming Energy and Buildings, Vol. 127 https://doi.org/10.1016/j.enbuild.2016.06.013	journal	September 2016
Fault detection and diagnosis for building cooling system with a tree-structured learning method Li, Dan; Zhou, Yuxun; Hu, Guoqiang Energy and Buildings, Vol. 127 https://doi.org/10.1016/j.enbuild.2016.06.017	journal	September 2016
A data-driven strategy for detection and diagnosis of building chiller faults using linear discriminant analysis Li, Dan; Hu, Guoqiang; Spanos, Costas J. Energy and Buildings, Vol. 128 https://doi.org/10.1016/j.enbuild.2016.07.014	journal	September 2016
A probabilistic approach to diagnose faults of air handling units in buildings Dey, Debashis; Dong, Bing Energy and Buildings, Vol. 130 https://doi.org/10.1016/j.enbuild.2016.08.017	journal	October 2016
PCA-based method of soft fault detection and identification for the ongoing commissioning of chillers Cotrufo, Nunzio; Zmeureanu, Radu Energy and Buildings, Vol. 130 https://doi.org/10.1016/j.enbuild.2016.08.083	journal	October 2016
A sensor fault detection and diagnosis strategy for screw chiller system using support vector data description-based D-statistic and DV-contribution plots Li, Guannan; Hu, Yunpeng; Chen, Huanxin Energy and Buildings, Vol. 133 https://doi.org/10.1016/j.enbuild.2016.09.037	journal	December 2016
A decision tree based data-driven diagnostic strategy for air handling units Yan, Rui; Ma, Zhenjun; Zhao, Yang Energy and Buildings, Vol. 133 https://doi.org/10.1016/j.enbuild.2016.09.039	journal	December 2016
Comparative investigations on reference models for fault detection and diagnosis in centrifugal chiller systems Tran, Dinh Anh Tuan; Chen, Youming; Jiang, Changliang Energy and Buildings, Vol. 133 https://doi.org/10.1016/j.enbuild.2016.09.062	journal	December 2016
An enhanced PCA method with Savitzky-Golay method for VRF system sensor fault detection and diagnosis Guo, Yabin; Li, Guannan; Chen, Huanxin Energy and Buildings, Vol. 142 https://doi.org/10.1016/j.enbuild.2017.03.026	journal	May 2017
Identification and isolation of outdoor fouling faults using only built-in sensors in variable refrigerant flow system: A data mining approach Li, Guannan; Hu, Yunpeng; Chen, Huanxin Energy and Buildings, Vol. 146 https://doi.org/10.1016/j.enbuild.2017.04.041	journal	July 2017
Advanced detection of HVAC faults using unsupervised SVM novelty detection and Gaussian process models Van Every, Philip Michael; Rodriguez, Mykel; Jones, C. Birk Energy and Buildings, Vol. 149 https://doi.org/10.1016/j.enbuild.2017.05.053	journal	August 2017
A machine learning bayesian network for refrigerant charge faults of variable refrigerant flow air conditioning system Hu, Min; Chen, Huanxin; Shen, Limei Energy and Buildings, Vol. 158 https://doi.org/10.1016/j.enbuild.2017.10.012	journal	January 2018
Fault detection and diagnosis for nonlinear systems: A new adaptive Gaussian mixture modeling approach Karami, Majid; Wang, Liping Energy and Buildings, Vol. 166 https://doi.org/10.1016/j.enbuild.2018.02.032	journal	May 2018
Improved sensor fault detection, diagnosis and estimation for screw chillers using density-based clustering and principal component analysis Li, Guannan; Hu, Yunpeng Energy and Buildings, Vol. 173 https://doi.org/10.1016/j.enbuild.2018.05.025	journal	August 2018
A reference architecture for the integration of automated energy performance fault diagnosis into HVAC systems Taal, Arie; Itard, Laure; Zeiler, Wim Energy and Buildings, Vol. 179 https://doi.org/10.1016/j.enbuild.2018.08.031	journal	November 2018
An enhanced PCA-based chiller sensor fault detection method using ensemble empirical mode decomposition based denoising Li, Guannan; Hu, Yunpeng Energy and Buildings, Vol. 183 https://doi.org/10.1016/j.enbuild.2018.10.013	journal	January 2019
Semi-supervised learning for early detection and diagnosis of various air handling unit faults Yan, Ke; Zhong, Chaowen; Ji, Zhiwei Energy and Buildings, Vol. 181 https://doi.org/10.1016/j.enbuild.2018.10.016	journal	December 2018
Novel real-time model-based fault detection method for automatic identification of abnormal energy performance in building ventilation units Bang, Mads; Engelsgaard, Sebastian Skals; Alexandersen, Emil Kjøller Energy and Buildings, Vol. 183 https://doi.org/10.1016/j.enbuild.2018.11.006	journal	January 2019
A proactive fault detection and diagnosis method for variable-air-volume terminals in building air conditioning systems Zhao, Yang; Li, Tingting; Fan, Cheng Energy and Buildings, Vol. 183 https://doi.org/10.1016/j.enbuild.2018.11.021	journal	January 2019
Early detection of faults in HVAC systems using an XGBoost model with a dynamic threshold Chakraborty, Debaditya; Elzarka, Hazem Energy and Buildings, Vol. 185 https://doi.org/10.1016/j.enbuild.2018.12.032	journal	February 2019
Development of clustering-based sensor fault detection and diagnosis strategy for chilled water system Luo, X. J.; Fong, K. F.; Sun, Y. J. Energy and Buildings, Vol. 186 https://doi.org/10.1016/j.enbuild.2019.01.006	journal	March 2019
A novel data-temporal attention network based strategy for fault diagnosis of chiller sensors Li, Ding; Li, Donghui; Li, Chengdong Energy and Buildings, Vol. 198 https://doi.org/10.1016/j.enbuild.2019.06.034	journal	September 2019
Fault detection in commercial building VAV AHU: A case study of an academic building Deshmukh, Suhrid; Samouhos, Stephen; Glicksman, Leon Energy and Buildings, Vol. 201 https://doi.org/10.1016/j.enbuild.2019.06.051	journal	October 2019
Assessment of the impact of HVAC system configuration and control zoning on thermal comfort and energy efficiency in flexible office spaces Gärtner, Jasmin Anika; Massa Gray, Francesco; Auer, Thomas Energy and Buildings, Vol. 212 https://doi.org/10.1016/j.enbuild.2020.109785	journal	April 2020
Data-driven and association rule mining-based fault diagnosis and action mechanism analysis for building chillers Liu, Jiangyan; Shi, Daliang; Li, Guannan Energy and Buildings, Vol. 216 https://doi.org/10.1016/j.enbuild.2020.109957	journal	June 2020
A comparison study of basic data-driven fault diagnosis methods for variable refrigerant flow system Zhou, Zhenxin; Li, Guannan; Wang, Jiangyu Energy and Buildings, Vol. 224 https://doi.org/10.1016/j.enbuild.2020.110232	journal	October 2020
Data-driven fault detection and diagnosis for packaged rooftop units using statistical machine learning classification methods Ebrahimifakhar, Amir; Kabirikopaei, Adel; Yuill, David Energy and Buildings, Vol. 225 https://doi.org/10.1016/j.enbuild.2020.110318	journal	October 2020
Ensemble learning with member optimization for fault diagnosis of a building energy system Han, Hua; Zhang, Zhan; Cui, Xiaoyu Energy and Buildings, Vol. 226 https://doi.org/10.1016/j.enbuild.2020.110351	journal	November 2020
Cluster analysis-based anomaly detection in building automation systems Gunay, H. Burak; Shi, Zixiao Energy and Buildings, Vol. 228 https://doi.org/10.1016/j.enbuild.2020.110445	journal	December 2020
Fault detection and diagnosis of large-scale HVAC systems in buildings using data-driven methods: A comprehensive review Mirnaghi, Maryam Sadat; Haghighat, Fariborz Energy and Buildings, Vol. 229 https://doi.org/10.1016/j.enbuild.2020.110492	journal	December 2020
Statistical characterization of semi-supervised neural networks for fault detection and diagnosis of air handling units Fan, Cheng; Liu, Xuyuan; Xue, Peng Energy and Buildings, Vol. 234 https://doi.org/10.1016/j.enbuild.2021.110733	journal	March 2021
Fault detection and diagnosis for Air Handling Unit based on multiscale convolutional neural networks Cheng, Fanyong; Cai, Wenjian; Zhang, Xin Energy and Buildings, Vol. 236 https://doi.org/10.1016/j.enbuild.2021.110795	journal	April 2021
Detection and diagnosis of AHU sensor faults using principal component analysis method Wang, Shengwei; Xiao, Fu Energy Conversion and Management, Vol. 45, Issue 17 https://doi.org/10.1016/j.enconman.2003.12.008	journal	October 2004
Sensor fault detection and validation of VAV terminals in air conditioning systems Wang, Shengwei; Qin, Jianying Energy Conversion and Management, Vol. 46, Issue 15-16 https://doi.org/10.1016/j.enconman.2004.11.011	journal	September 2005
Data mining based sensor fault diagnosis and validation for building air conditioning system Hou, Zhijian; Lian, Zhiwei; Yao, Ye Energy Conversion and Management, Vol. 47, Issue 15-16 https://doi.org/10.1016/j.enconman.2005.11.010	journal	September 2006
Detection and diagnosis for sensor fault in HVAC systems Du, Zhimin; Jin, Xinqiao Energy Conversion and Management, Vol. 48, Issue 3 https://doi.org/10.1016/j.enconman.2006.09.023	journal	March 2007
Multiple faults diagnosis for sensors in air handling unit using Fisher discriminant analysis Du, Zhimin; Jin, Xinqiao Energy Conversion and Management, Vol. 49, Issue 12 https://doi.org/10.1016/j.enconman.2008.06.032	journal	December 2008
Data-driven based reliability evaluation for measurements of sensors in a vapor compression system Du, Zhimin; Chen, Ling; Jin, Xinqiao Energy, Vol. 122 https://doi.org/10.1016/j.energy.2017.01.055	journal	March 2017
Knowledge discovery of data-driven-based fault diagnostics for building energy systems: A case study of the building variable refrigerant flow system Liu, Jiangyan; Li, Guannan; Liu, Bin Energy, Vol. 174 https://doi.org/10.1016/j.energy.2019.02.161	journal	May 2019
Refrigerant charge fault detection method of air source heat pump system using convolutional neural network for energy saving Eom, Yong Hwan; Yoo, Jin Woo; Hong, Sung Bin Energy, Vol. 187 https://doi.org/10.1016/j.energy.2019.115877	journal	November 2019
A meta-model-based optimization approach for fast and reliable calibration of building energy models Chen, Jianli; Gao, Xinghua; Hu, Yuqing Energy, Vol. 188 https://doi.org/10.1016/j.energy.2019.116046	journal	December 2019
Real-world application of machine-learning-based fault detection trained with experimental data Bode, Gerrit; Thul, Simon; Baranski, Marc Energy, Vol. 198 https://doi.org/10.1016/j.energy.2020.117323	journal	May 2020
Deep learning based reference model for operational risk evaluation of screw chillers for energy efficiency Zhu, Xu; Zhang, Shuai; Jin, Xinqiao Energy, Vol. 213 https://doi.org/10.1016/j.energy.2020.118833	journal	December 2020
Fault diagnosis and refrigerant leak detection in vapour compression refrigeration systems Tassou, S. A.; Grace, I. N. International Journal of Refrigeration, Vol. 28, Issue 5 https://doi.org/10.1016/j.ijrefrig.2004.12.007	journal	August 2005
Decoupling features and virtual sensors for diagnosis of faults in vapor compression air conditioners Li, Haorong; Braun, James E. International Journal of Refrigeration, Vol. 30, Issue 3 https://doi.org/10.1016/j.ijrefrig.2006.07.024	journal	May 2007
Model-based Fault Detection and Diagnosis of HVAC systems using Support Vector Machine method Liang, J.; Du, R. International Journal of Refrigeration, Vol. 30, Issue 6 https://doi.org/10.1016/j.ijrefrig.2006.12.012	journal	September 2007
Important sensors for chiller fault detection and diagnosis (FDD) from the perspective of feature selection and machine learning Han, H.; Gu, B.; Wang, T. International Journal of Refrigeration, Vol. 34, Issue 2 https://doi.org/10.1016/j.ijrefrig.2010.08.011	journal	March 2011
Fault and sensor error diagnostic strategies for a vapor compression refrigeration system by using fuzzy inference systems and artificial neural network Kocyigit, Necati International Journal of Refrigeration, Vol. 50 https://doi.org/10.1016/j.ijrefrig.2014.10.017	journal	February 2015
Fault detection and diagnosis method for cooling dehumidifier based on LS-SVM NARX model Gao, Yunguang; Liu, Shunbo; Li, Feng International Journal of Refrigeration, Vol. 61 https://doi.org/10.1016/j.ijrefrig.2015.08.020	journal	January 2016
Sensitivity analysis for PCA-based chiller sensor fault detection Hu, Yunpeng; Li, Guannan; Chen, Huanxin International Journal of Refrigeration, Vol. 63 https://doi.org/10.1016/j.ijrefrig.2015.11.006	journal	March 2016
A practical chiller fault diagnosis method based on discrete Bayesian network Wang, Yalan; Wang, Zhiwei; He, Suowei International Journal of Refrigeration, Vol. 102 https://doi.org/10.1016/j.ijrefrig.2019.03.008	journal	June 2019
Gradual fault early stage diagnosis for air source heat pump system using deep learning techniques Sun, Zhe; Jin, Huaqiang; Gu, Jiangping International Journal of Refrigeration, Vol. 107 https://doi.org/10.1016/j.ijrefrig.2019.07.020	journal	November 2019
Fault diagnosis of VRF air-conditioning system based on improved Gaussian mixture model with PCA approach Guo, Yabin; Chen, Huanxin International Journal of Refrigeration, Vol. 118 https://doi.org/10.1016/j.ijrefrig.2020.06.009	journal	October 2020
A model-based online fault detection and diagnosis strategy for centrifugal chiller systems Cui, Jingtan; Wang, Shengwei International Journal of Thermal Sciences, Vol. 44, Issue 10 https://doi.org/10.1016/j.ijthermalsci.2005.03.004	journal	October 2005
Sensor data validation and fault diagnosis using Auto-Associative Neural Network for HVAC systems Elnour, Mariam; Meskin, Nader; Al-Naemi, Mohammed Journal of Building Engineering, Vol. 27 https://doi.org/10.1016/j.jobe.2019.100935	journal	January 2020
A computationally efficient method for fault diagnosis of fan-coil unit terminals in building Heating Ventilation and Air Conditioning systems Ranade, Akshay; Provan, Gregory; El-Din Mady, Alie Journal of Building Engineering, Vol. 27 https://doi.org/10.1016/j.jobe.2019.100955	journal	January 2020
Bilinear model-based diagnosis of lock-in-place failures of variable-air-volume HVAC systems of multizone buildings A., Mona Subramaniam; Jain, Tushar; Yamé, Joseph J. Journal of Building Engineering, Vol. 28 https://doi.org/10.1016/j.jobe.2019.101023	journal	March 2020
Fault detection and diagnosis in air handling using data-driven methods Montazeri, Atena; Kargar, Seyed Mohamad Journal of Building Engineering, Vol. 31 https://doi.org/10.1016/j.jobe.2020.101388	journal	September 2020
Artificial intelligence assisted false alarm detection and diagnosis system development for reducing maintenance cost of chillers at the data centre Lee, Dasheng; Lai, Chih-Wei; Liao, Kuo-Kai Journal of Building Engineering, Vol. 36 https://doi.org/10.1016/j.jobe.2020.102110	journal	April 2021
A data-driven fault detection and diagnosis scheme for air handling units in building HVAC systems considering undefined states Yun, Woo-Seung; Hong, Won-Hwa; Seo, Hyuncheol Journal of Building Engineering, Vol. 35 https://doi.org/10.1016/j.jobe.2020.102111	journal	March 2021
Online fault detection methods for chillers combining extended kalman filter and recursive one-class SVM Yan, Ke; Ji, Zhiwei; Shen, Wen Neurocomputing, Vol. 228 https://doi.org/10.1016/j.neucom.2016.09.076	journal	March 2017
Assessing barriers and research challenges for automated fault detection and diagnosis technology for small commercial buildings in the United States Frank, Stephen; Jin, Xin; Studer, Daniel Renewable and Sustainable Energy Reviews, Vol. 98 https://doi.org/10.1016/j.rser.2018.08.046	journal	December 2018
Artificial intelligence-based fault detection and diagnosis methods for building energy systems: Advantages, challenges and the future Zhao, Yang; Li, Tingting; Zhang, Xuejun Renewable and Sustainable Energy Reviews, Vol. 109 https://doi.org/10.1016/j.rser.2019.04.021	journal	July 2019
A study on semi-supervised learning in enhancing performance of AHU unseen fault detection with limited labeled data Fan, Cheng; Liu, Yichen; Liu, Xuyuan Sustainable Cities and Society, Vol. 70 https://doi.org/10.1016/j.scs.2021.102874	journal	July 2021
Least Squares Support Vector Machine Classifiers Suykens, J. A. K.; Vandewalle, J. Neural Processing Letters, Vol. 9, Issue 3, p. 293-300 https://doi.org/10.1023/A:1018628609742	journal	June 1999
Fault Diagnosis in Air-Conditioning Systems: A Multi-Step Fuzzy Model-Based Approach Dexter, A. L.; Ngo, D. HVAC&R Research, Vol. 7, Issue 1 https://doi.org/10.1080/10789669.2001.10391431	journal	January 2001
A Novel Strategy for the Fault Detection and Diagnosis of Centrifugal Chiller Systems Zhou, Qiang; Wang, Shengwei; Xiao, Fu HVAC&R Research, Vol. 15, Issue 1 https://doi.org/10.1080/10789669.2009.10390825	journal	January 2009
Model-based real-time whole building energy performance monitoring and diagnostics O'Neill, Zheng; Pang, Xiufeng; Shashanka, Madhusudana Journal of Building Performance Simulation, Vol. 7, Issue 2 https://doi.org/10.1080/19401493.2013.777118	journal	April 2013
Building Energy Doctors: An SPC and Kalman Filter-Based Method for System-Level Fault Detection in HVAC Systems Sun, Biao; Luh, Peter B.; Jia, Qing-Shan IEEE Transactions on Automation Science and Engineering, Vol. 11, Issue 1 https://doi.org/10.1109/TASE.2012.2226155	journal	January 2014
Sensor Fault Detection and Diagnosis Method for AHU Using 1-D CNN and Clustering Analysis Liu, Jingjing; Zhang, Min; Wang, Hai Computational Intelligence and Neuroscience, Vol. 2019 https://doi.org/10.1155/2019/5367217	journal	September 2019
Representing Small Commercial Building Faults in EnergyPlus, Part I: Model Development Kim, Janghyun; Frank, Stephen; Braun, James E. Buildings, Vol. 9, Issue 11 https://doi.org/10.3390/buildings9110233	journal	November 2019

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