Artificial intelligence-based fault detection and diagnosis methods for building energy systems: Advantages, challenges and the future

ImageNet: A large-scale hierarchical image database

Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333 · see citing works

Support-vector networks

Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421 · see citing works

Support-Vector Networks

Cortes, C., & Vapnik, V. N. (1995) · Machine Learning · doi:10.1023/a:1022627411411 · Cited by 34,565 · see citing works

Support Vector Regression Machines

Drucker, H., Burges, C. J. C., Kaufman, L., Smola, A. J., & Vapnik, V. N. (1996) · Cited by 4,654 · see citing works

Fusion, propagation, and structuring in belief networks

Pearl, J. (1986) · Artificial Intelligence · doi:10.1016/0004-3702(86)90072-x · Cited by 2,157 · see citing works

Support vector domain description

Tax, D. M. J., & Duin, R. P. W. (1999) · Pattern Recognition Letters · doi:10.1016/s0167-8655(99)00087-2 · Cited by 1,632 · see citing works

Review Article : Methods for Fault Detection, Diagnostics, and Prognostics for Building Systems—A Review, Part I

Katipamula, S., & Brambley, M. R. (2005) · Science and Technology for the Built Environment · doi:10.1080/10789669.2005.10391123 · Cited by 942 · see citing works

The European Control Conference

(2013) · European Control Conference (ECC) · doi:10.23919/ecc.2013.6669086 · Cited by 558 · see citing works

A review of fault detection and diagnostics methods for building systems

Kim, W., & Katipamula, S. (2017) · Science and Technology for the Built Environment · doi:10.1080/23744731.2017.1318008 · Cited by 388 · see citing works

A Survey of Recent Trends in One Class Classification

Khan, S. S., & Madden, M. G. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-17080-5_21 · Cited by 348 · see citing works

2015 54th IEEE Conference on Decision and Control (CDC)

(2015) · doi:10.1109/cdc35581.2015 · Cited by 348 · see citing works

Multi-source information fusion based fault diagnosis of ground-source heat pump using Bayesian network

Cai, B., Liu, Y., Fan, Q., et al. (2013) · Applied Energy · doi:10.1016/j.apenergy.2013.09.043 · Cited by 330 · see citing works

A rule-based fault detection method for air handling units

Schein, J., Bushby, S. T., Castro, N. S., et al. (2006) · Energy and Buildings · doi:10.1016/j.enbuild.2006.04.014 · Cited by 322 · see citing works

Review Article : Methods for Fault Detection, Diagnostics, and Prognostics for Building Systems—A Review, Part II

Katipamula, S., & Brambley, M. R. (2005) · Science and Technology for the Built Environment · doi:10.1080/10789669.2005.10391133 · Cited by 310 · see citing works

A review of fault detection and diagnosis methodologies on air-handling units

Yu, Y., Woradechjumroen, D., & Yu, D. (2014) · Energy and Buildings · doi:10.1016/j.enbuild.2014.06.042 · Cited by 307 · see citing works

Fault detection and diagnosis for buildings and HVAC systems using combined neural networks and subtractive clustering analysis

Du, Z., Fan, B., Jin, X., & Chi, J. (2013) · Building and Environment · doi:10.1016/j.buildenv.2013.11.021 · Cited by 296 · see citing works

Pattern recognition-based chillers fault detection method using Support Vector Data Description (SVDD)

Zhao, Y., Wang, S., & Xiao, F. (2013) · Applied Energy · doi:10.1016/j.apenergy.2012.12.043 · Cited by 278 · see citing works

Data mining in building automation system for improving building operational performance

Xiao, F., & Fan, C. (2014) · Energy and Buildings · doi:10.1016/j.enbuild.2014.02.005 · Cited by 258 · see citing works

Model-based Fault Detection and Diagnosis of HVAC systems using Support Vector Machine method

Liang, J., & Du, R. (2007) · International Journal of Refrigeration · doi:10.1016/j.ijrefrig.2006.12.012 · Cited by 250 · see citing works

A model-based fault detection and diagnostic methodology based on PCA method and wavelet transform

Li, S., & Wen, J. (2013) · Energy and Buildings · doi:10.1016/j.enbuild.2013.08.044 · Cited by 248 · see citing works

AHU sensor fault diagnosis using principal component analysis method

Wang, S., & Xiao, F. (2003) · Energy and Buildings · doi:10.1016/j.enbuild.2003.10.002 · Cited by 231 · see citing works

A framework for knowledge discovery in massive building automation data and its application in building diagnostics

Fan, C., Xiao, F., & Yan, C. (2014) · Automation in Construction · doi:10.1016/j.autcon.2014.12.006 · Cited by 218 · see citing works

An intelligent chiller fault detection and diagnosis methodology using Bayesian belief network

Zhao, Y., Xiao, F., & Wang, S. (2012) · Energy and Buildings · doi:10.1016/j.enbuild.2012.11.007 · Cited by 215 · see citing works

Machine learning-based thermal response time ahead energy demand prediction for building heating systems

Guo, Y., Wang, J., Chen, H., Li, G., Liu, J., Xu, C., Huang, R., & Huang, Y. (2018) · Applied Energy · doi:10.1016/j.apenergy.2018.03.125 · Cited by 207 · see citing works

2008 Renewable Energy Data Book

National Renewable Energy Lab. (NREL), G., Beiter, P., Energy Analysis (EI-30) (Energy Analysis Corporate) USDOE Office of Energy Efficiency and Renewable Energy (EERE), et al. (2009) · doi:10.2172/1339347 · Cited by 206 · see citing works

Unsupervised data analytics in mining big building operational data for energy efficiency enhancement: A review

Fan, C., Xiao, F., Li, C. Z., & Wang, J. (2017) · Energy and Buildings · doi:10.1016/j.enbuild.2017.11.008 · Cited by 203 · see citing works

Sensor-fault detection, diagnosis and estimation for centrifugal chiller systems using principal-component analysis method

Wang, S., & Cui, J. (2005) · Applied Energy · doi:10.1016/j.apenergy.2004.11.002 · Cited by 201 · see citing works

Fault detection analysis using data mining techniques for a cluster of smart office buildings

Capozzoli, A., Lauro, F., & Khan, I. (2015) · Expert Systems with Applications · doi:10.1016/j.eswa.2015.01.010 · Cited by 197 · see citing works

Data-driven Fault Detection and Diagnosis for HVAC water chillers

Beghi, A., Brignoli, R., Cecchinato, L., et al. (2016) · Control Engineering Practice · doi:10.1016/j.conengprac.2016.04.018 · Cited by 197 · see citing works

Diagnostic Bayesian networks for diagnosing air handling units faults – part I: Faults in dampers, fans, filters and sensors

Zhao, Y., Wen, J., Xiao, F., et al. (2016) · Applied Thermal Engineering · doi:10.1016/j.applthermaleng.2015.09.121 · Cited by 190 · see citing works

A decision tree based data-driven diagnostic strategy for air handling units

Yan, R., Ma, Z., Zhao, Y., & Kokogiannakis, G. (2016) · Energy and Buildings · doi:10.1016/j.enbuild.2016.09.039 · Cited by 180 · see citing works

Subsystem level fault diagnosis of a building's air-handling unit using general regression neural networks

Lee, W., House, J. M., & Kyong, N.-H. (2003) · Applied Energy · doi:10.1016/s0306-2619(03)00107-7 · Cited by 177 · see citing works

Modeling of HVAC operational faults in building performance simulation

Zhang, R., & Hong, T. (2017) · Applied Energy · doi:10.1016/j.apenergy.2017.05.153 · Cited by 173 · see citing works

Deep learning-based fault diagnosis of variable refrigerant flow air-conditioning system for building energy saving

Guo, Y., Tan, Z., Chen, H., Li, G., Wang, J., Huang, R., Liu, J., & Ahmad, T. (2018) · Applied Energy · doi:10.1016/j.apenergy.2018.05.075 · Cited by 171 · see citing works

Cost-sensitive and sequential feature selection for chiller fault detection and diagnosis

Yan, K., Ma, L., Dai, Y., Shen, W., Ji, Z., & Xie, D. (2017) · International Journal of Refrigeration · doi:10.1016/j.ijrefrig.2017.11.003 · Cited by 162 · see citing works

A critical review of fault modeling of HVAC systems in buildings

Li, Y., & O’Neill, Z. D. (2018) · Building Simulation · doi:10.1007/s12273-018-0458-4 · Cited by 162 · see citing works

ARX model based fault detection and diagnosis for chillers using support vector machines

Yan, K., Shen, W., Mulumba, T., et al. (2014) · Energy and Buildings · doi:10.1016/j.enbuild.2014.05.049 · Cited by 154 · see citing works

A system-level fault detection and diagnosis strategy for HVAC systems involving sensor faults

Wang, S., Zhou, Q., & Xiao, F. (2009) · Energy and Buildings · doi:10.1016/j.enbuild.2009.10.017 · Cited by 150 · see citing works

Online fault detection methods for chillers combining extended kalman filter and recursive one-class SVM

Yan, K., Ji, Z., & Shen, W. (2016) · Neurocomputing · doi:10.1016/j.neucom.2016.09.076 · Cited by 149 · see citing works

A novel methodology for knowledge discovery through mining associations between building operational data

Yu, Z. J., Haghighat, F., Fung, B. C. M., & Zhou, L. G. (2011) · Energy and Buildings · doi:10.1016/j.enbuild.2011.12.018 · Cited by 143 · see citing works

Temporal knowledge discovery in big BAS data for building energy management

Fan, C., Xiao, F., Madsen, H., & Wang, D. (2015) · Energy and Buildings · doi:10.1016/j.enbuild.2015.09.060 · Cited by 142 · see citing works

Diagnostic Bayesian networks for diagnosing air handling units faults – Part II: Faults in coils and sensors

Zhao, Y., Wen, J., & Wang, S. (2015) · Applied Thermal Engineering · doi:10.1016/j.applthermaleng.2015.07.001 · Cited by 141 · see citing works

Fault diagnosis for temperature, flow rate and pressure sensors in VAV systems using wavelet neural network

Du, Z., Jin, X., & Yang, Y. (2009) · Applied Energy · doi:10.1016/j.apenergy.2009.01.015 · Cited by 141 · see citing works

A model-based online fault detection and diagnosis strategy for centrifugal chiller systems

Cui, J., & Wang, S. (2005) · International Journal of Thermal Sciences · doi:10.1016/j.ijthermalsci.2005.03.004 · Cited by 139 · see citing works

Bayesian network based FDD strategy for variable air volume terminals

Xiao, F., Zhao, Y., Wen, J., et al. (2013) · Automation in Construction · doi:10.1016/j.autcon.2013.10.019 · Cited by 138 · see citing works

Fault detection and diagnosis of chillers using Bayesian network merged distance rejection and multi-source non-sensor information

Wang, Z., Wang, Z., He, S., Gu, X., & Yan, Z. F. (2017) · Applied Energy · doi:10.1016/j.apenergy.2016.11.130 · Cited by 138 · see citing works

Fault detection and operation optimization in district heating substations based on data mining techniques

Xue, P., Zhou, Z., Fang, X., et al. (2017) · Applied Energy · doi:10.1016/j.apenergy.2017.08.035 · Cited by 133 · see citing works

A hybrid FDD strategy for local system of AHU based on artificial neural network and wavelet analysis

Fan, B., Du, Z., Jin, X., et al. (2010) · Building and Environment · doi:10.1016/j.buildenv.2010.05.031 · Cited by 132 · see citing works

Important sensors for chiller fault detection and diagnosis (FDD) from the perspective of feature selection and machine learning

Han, H., Gu, B., Wang, T., et al. (2010) · International Journal of Refrigeration · doi:10.1016/j.ijrefrig.2010.08.011 · Cited by 132 · see citing works

A statistical fault detection and diagnosis method for centrifugal chillers based on exponentially-weighted moving average control charts and support vector regression

Zhao, Y., Wang, S., & Xiao, F. (2012) · Applied Thermal Engineering · doi:10.1016/j.applthermaleng.2012.09.030 · Cited by 131 · see citing works

An improved fault detection method for incipient centrifugal chiller faults using the PCA-R-SVDD algorithm

Li, G., Hu, Y., Chen, H., et al. (2015) · Energy and Buildings · doi:10.1016/j.enbuild.2015.12.045 · Cited by 131 · see citing works

Study on a hybrid SVM model for chiller FDD applications

Han, H., Gu, B., Kang, J., et al. (2010) · Applied Thermal Engineering · doi:10.1016/j.applthermaleng.2010.10.021 · Cited by 124 · see citing works

Enhanced chiller sensor fault detection, diagnosis and estimation using wavelet analysis and principal component analysis methods

Xu, X., Xiao, F., & Wang, S. (2007) · Applied Thermal Engineering · doi:10.1016/j.applthermaleng.2007.03.021 · Cited by 123 · see citing works

Monitoring-based HVAC commissioning of an existing office building for energy efficiency

Wang, L., Greenberg, S., Fiegel, J., et al. (2012) · Applied Energy · doi:10.1016/j.apenergy.2012.09.005 · Cited by 119 · see citing works

Building Energy Doctors: An SPC and Kalman Filter-Based Method for System-Level Fault Detection in HVAC Systems

Sun, B., Luh, P. B., Jia, Q., et al. (2013) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2012.2226155 · Cited by 117 · see citing works

PCA-SVM-Based Automated Fault Detection and Diagnosis (AFDD) for Vapor-Compression Refrigeration Systems

Han, H., Cao, Z., Gu, B. Q., et al. (2010) · Science and Technology for the Built Environment · doi:10.1080/10789669.2010.10390906 · Cited by 116 · see citing works

A methodology for identifying and improving occupant behavior in residential buildings

Yu, Z. J., Haghighat, F., Fung, B. C. M., et al. (2011) · Energy · doi:10.1016/j.energy.2011.09.002 · Cited by 114 · see citing works

Residential HVAC fault detection using a system identification approach

Turner, W. J. N., Staino, A., & Basu, B. (2017) · Energy and Buildings · doi:10.1016/j.enbuild.2017.06.008 · Cited by 112 · see citing works

A fault detection and diagnosis strategy of VAV air-conditioning systems for improved energy and control performances

Qin, J., & Wang, S. (2005) · Energy and Buildings · doi:10.1016/j.enbuild.2004.12.011 · Cited by 110 · see citing works

Detection and diagnosis of AHU sensor faults using principal component analysis method

Wang, S., & Xiao, F. (2004) · Energy Conversion and Management · doi:10.1016/j.enconman.2003.12.008 · Cited by 107 · see citing works

Fault detection and diagnosis based on improved PCA with JAA method in VAV systems

Du, Z., Jin, X., & Wu, L. (2006) · Building and Environment · doi:10.1016/j.buildenv.2006.08.011 · Cited by 107 · see citing works

Improved sensor fault detection, diagnosis and estimation for screw chillers using density-based clustering and principal component analysis

Li, G., & Hu, Y. (2018) · Energy and Buildings · doi:10.1016/j.enbuild.2018.05.025 · Cited by 104 · see citing works

Data association mining for identifying lighting energy waste patterns in educational institutes

Cabrera, D. M., & Zareipour, H. (2013) · Energy and Buildings · doi:10.1016/j.enbuild.2013.02.049 · Cited by 103 · see citing works

Data partitioning and association mining for identifying VRF energy consumption patterns under various part loads and refrigerant charge conditions

Li, G., Hu, Y., Chen, H., et al. (2016) · Applied Energy · doi:10.1016/j.apenergy.2016.10.091 · Cited by 100 · see citing works

A novel efficient SVM-based fault diagnosis method for multi-split air conditioning system’s refrigerant charge fault amount

Sun, K., Li, G., Chen, H., et al. (2016) · Applied Thermal Engineering · doi:10.1016/j.applthermaleng.2016.07.109 · Cited by 98 · see citing works

Fault detection and diagnosis for building cooling system with a tree-structured learning method

Li, D., Zhou, Y., Hu, G., et al. (2016) · Energy and Buildings · doi:10.1016/j.enbuild.2016.06.017 · Cited by 97 · see citing works

A sensor fault detection strategy for air handling units using cluster analysis

Yan, R., Ma, Z., Kokogiannakis, G., & Zhao, Y. (2016) · Automation in Construction · doi:10.1016/j.autcon.2016.06.005 · Cited by 97 · see citing works

Automated FDD of multiple-simultaneous faults (MSF) and the application to building chillers

Han, H., Gu, B. Q., Hong, Y., et al. (2011) · Energy and Buildings · doi:10.1016/j.enbuild.2011.06.011 · Cited by 94 · see citing works

Sensor fault detection and its efficiency analysis in air handling unit using the combined neural networks

Du, Z., Fan, B., Chi, J., et al. (2014) · Energy and Buildings · doi:10.1016/j.enbuild.2013.12.038 · Cited by 93 · see citing works

Application of machine learning in the fault diagnostics of air handling units

Najafi, M., Auslander, D. M., Bartlett, P. L., et al. (2012) · Applied Energy · doi:10.1016/j.apenergy.2012.02.049 · Cited by 92 · see citing works

An enhanced PCA method with Savitzky-Golay method for VRF system sensor fault detection and diagnosis

Guo, Y., Li, G., Chen, H., et al. (2017) · Energy and Buildings · doi:10.1016/j.enbuild.2017.03.026 · Cited by 91 · see citing works

Fault diagnosis of an air-handling unit using artificial neural networks

Lee, W. Y., House, J. M., Park, C., et al. (1996) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · Cited by 89 · see citing works

Multiple faults diagnosis for sensors in air handling unit using Fisher discriminant analysis

Du, Z., & Jin, X. (2008) · Energy Conversion and Management · doi:10.1016/j.enconman.2008.06.032 · Cited by 89 · see citing works

Fault Diagnosis in Air-Conditioning Systems: A Multi-Step Fuzzy Model-Based Approach

Dexter, A. L., & Ngo, D. (2001) · Science and Technology for the Built Environment · doi:10.1080/10789669.2001.10391431 · Cited by 88 · see citing works

Fault and sensor error diagnostic strategies for a vapor compression refrigeration system by using fuzzy inference systems and artificial neural network

Koçyiğit, N. (2014) · International Journal of Refrigeration · doi:10.1016/j.ijrefrig.2014.10.017 · Cited by 87 · see citing works

A Hierarchical Rule-Based Fault Detection and Diagnostic Method for HVAC Systems

Schein, J., & Bushby, S. T. (2006) · Science and Technology for the Built Environment · doi:10.1080/10789669.2006.10391170 · Cited by 85 · see citing works

Fault detection and diagnosis of chiller using Bayesian network classifier with probabilistic boundary

He, S., Wang, Z., Wang, Z., et al. (2016) · Applied Thermal Engineering · doi:10.1016/j.applthermaleng.2016.06.153 · Cited by 85 · see citing works

Diagnostic tools of energy performance for supermarkets using Artificial Neural Network algorithms

Mavromatidis, G., Acha, S., & Shah, N. (2013) · Energy and Buildings · doi:10.1016/j.enbuild.2013.03.020 · Cited by 84 · see citing works

The performance prediction of ground source heat pump system based on monitoring data and data mining technology

Yan, L., Hu, P., Changhong, L., et al. (2016) · Energy and Buildings · doi:10.1016/j.enbuild.2016.06.055 · Cited by 84 · see citing works

Advanced detection of HVAC faults using unsupervised SVM novelty detection and Gaussian process models

Every, P. M. V., Rodriguez, M., Jones, C. B., Mammoli, A. A., & Martínez‐Ramón, M. (2017) · Energy and Buildings · doi:10.1016/j.enbuild.2017.05.053 · Cited by 84 · see citing works

An evaluation of empirically-based models for predicting energy performance of vapor-compression water chillers

Lee, T.-S., & Lu, W.-C. (2010) · Applied Energy · doi:10.1016/j.apenergy.2010.05.005 · Cited by 83 · see citing works

Modelling of vapour-compression liquid chillers with neural networks

Swider, D. J., Browne, M. W., Bansal, P. K., et al. (2001) · Applied Thermal Engineering · doi:10.1016/s1359-4311(00)00036-3 · Cited by 83 · see citing works

Development of an RDP neural network for building energy consumption fault detection and diagnosis

Magoulès, F., Zhao, H., & Elizondo, D. A. (2013) · Energy and Buildings · doi:10.1016/j.enbuild.2013.02.050 · Cited by 83 · see citing works

Using the fuzzy linear regression method to benchmark the energy efficiency of commercial buildings

Chung, W. (2012) · Applied Energy · doi:10.1016/j.apenergy.2012.01.061 · Cited by 81 · see citing works

Optimized neural network-based fault diagnosis strategy for VRF system in heating mode using data mining

Guo, Y., Li, G., Chen, H., et al. (2017) · Applied Thermal Engineering · doi:10.1016/j.applthermaleng.2017.07.065 · Cited by 79 · see citing works

A fault detection technique for air-source heat pump water chiller/heaters

Chen, Y., & Lan, L. (2009) · Energy and Buildings · doi:10.1016/j.enbuild.2009.03.007 · Cited by 78 · see citing works

A One-Class SVM Based Tool for Machine Learning Novelty Detection in HVAC Chiller Systems

Beghi, A., Cecchinato, L., Corazzol, C., et al. (2014) · IFAC Proceedings Volumes · doi:10.3182/20140824-6-za-1003.02382 · Cited by 77 · see citing works

A sensor fault detection and diagnosis strategy for screw chiller system using support vector data description-based D-statistic and DV-contribution plots

Li, G., Hu, Y., Chen, H., et al. (2016) · Energy and Buildings · doi:10.1016/j.enbuild.2016.09.037 · Cited by 77 · see citing works

Online model-based fault detection and diagnosis strategy for VAV air handling units

Wang, H., Chen, Y., Chan, C. W. H., et al. (2012) · Energy and Buildings · doi:10.1016/j.enbuild.2012.08.016 · Cited by 76 · see citing works

A robust pattern recognition-based fault detection and diagnosis (FDD) method for chillers

Zhao, Y., Xiao, F., Wen, J., et al. (2014) · Science and Technology for the Built Environment · doi:10.1080/10789669.2014.938006 · Cited by 74 · see citing works

A Novel Strategy for the Fault Detection and Diagnosis of Centrifugal Chiller Systems

Zhou, Q., Wang, S., & Xiao, F. (2009) · Science and Technology for the Built Environment · doi:10.1080/10789669.2009.10390825 · Cited by 74 · see citing works

Fault diagnosis for sensors in air handling unit based on neural network pre-processed by wavelet and fractal

Zhu, Y., Jin, X., & Du, Z. (2011) · Energy and Buildings · doi:10.1016/j.enbuild.2011.09.043 · Cited by 74 · see citing works

Sensor fault detection and validation of VAV terminals in air conditioning systems

Wang, S., & Qin, J. (2005) · Energy Conversion and Management · doi:10.1016/j.enconman.2004.11.011 · Cited by 73 · see citing works

Cross-level fault detection and diagnosis of building HVAC systems

Wu, S., & Sun, J. (2011) · Building and Environment · doi:10.1016/j.buildenv.2011.01.017 · Cited by 73 · see citing works

Proceedings of the 1st ACM Conference on Embedded Systems for Energy-Efficient Buildings

(2014) · doi:10.1145/2674061 · Cited by 72 · see citing works

Feature selection based on Bayesian network for chiller fault diagnosis from the perspective of field applications

Wang, Z., Wang, Z., Wang, Z., et al. (2017) · Applied Thermal Engineering · doi:10.1016/j.applthermaleng.2017.10.079 · Cited by 70 · see citing works

Data driven investigation of faults in HVAC systems with model, cluster and compare (MCC)

Narayanaswamy, B., Balaji, B., Gupta, R. K., et al. (2014) · doi:10.1145/2674061.2674067 · Cited by 69 · see citing works

Fault detection and diagnosis for nonlinear systems: A new adaptive Gaussian mixture modeling approach

Karami, M., & Wang, L. (2018) · Energy and Buildings · doi:10.1016/j.enbuild.2018.02.032 · Cited by 68 · see citing works

A diagnostic tool for online sensor health monitoring in air-conditioning systems

Xiao, F., Wang, S., & Zhang, J. (2005) · Automation in Construction · doi:10.1016/j.autcon.2005.06.001 · Cited by 67 · see citing works

An online fault diagnosis tool of VAV terminals for building management and control systems

Wang, H., Chen, Y., Chan, C. W. H., et al. (2011) · Automation in Construction · doi:10.1016/j.autcon.2011.06.018 · Cited by 66 · see citing works

Development and alpha testing of a cloud based automated fault detection and diagnosis tool for Air Handling Units

Bruton, K., Raftery, P., O’Donovan, P., Aughney, N., Keane, M. M., & O’Sullivan, D. T. J. (2014) · Automation in Construction · doi:10.1016/j.autcon.2013.12.006 · Cited by 64 · see citing works

Discovering gradual patterns in building operations for improving building energy efficiency

Fan, C., Sun, Y., Shan, K., Xiao, F., & Wang, J. (2018) · Applied Energy · doi:10.1016/j.apenergy.2018.04.118 · Cited by 64 · see citing works

Detection and diagnosis for sensor fault in HVAC systems

Du, Z., & Jin, X. (2006) · Energy Conversion and Management · doi:10.1016/j.enconman.2006.09.023 · Cited by 63 · see citing works

Sequential rule based algorithms for temperature sensor fault detection in air handling units

Hooncheul, Y., Cho, S., Tae, C.-S., et al. (2008) · Energy Conversion and Management · doi:10.1016/j.enconman.2008.01.029 · Cited by 62 · see citing works

Fault diagnosis for a solar assisted heat pump system under incomplete data and expert knowledge

Liu, Z., Liu, Y., Zhang, D., et al. (2015) · Energy · doi:10.1016/j.energy.2015.04.090 · Cited by 62 · see citing works

A robot fault diagnostic tool for flow rate sensors in air dampers and VAV terminals

Du, Z., Jin, X., & Yang, X. (2008) · Energy and Buildings · doi:10.1016/j.enbuild.2008.09.007 · Cited by 60 · see citing works

A robust fault detection and diagnosis strategy for multiple faults of VAV air handling units

Wang, H., & Chen, Y. (2016) · Energy and Buildings · doi:10.1016/j.enbuild.2016.06.013 · Cited by 59 · see citing works

An efficient VRF system fault diagnosis strategy for refrigerant charge amount based on PCA and dual neural network model

Shi, S., Li, G., Chen, H., Hu, Y., Wang, X., Guo, Y., & Sun, S. (2017) · Applied Thermal Engineering · doi:10.1016/j.applthermaleng.2017.09.117 · Cited by 59 · see citing works

A virtual supply airflow rate meter for rooftop air-conditioning units

Yu, D., Li, H., & Yang, M. (2010) · Building and Environment · doi:10.1016/j.buildenv.2010.12.017 · Cited by 58 · see citing works

Transient pattern analysis for fault detection and diagnosis of HVAC systems

Cho, S.-H., Yang, H.-C., Zaheeruddin, M., et al. (2005) · Energy Conversion and Management · doi:10.1016/j.enconman.2005.02.012 · Cited by 55 · see citing works

An enhanced chiller FDD strategy based on the combination of the LSSVR-DE model and EWMA control charts

Tran, D. A. T., Chen, Y., Ao, H., et al. (2016) · International Journal of Refrigeration · doi:10.1016/j.ijrefrig.2016.07.024 · Cited by 54 · see citing works

Fault diagnosis for heat pumps with parameter identification and clustering

Zogg, D., Shafai, E., & Geering, H. P. (2005) · Control Engineering Practice · doi:10.1016/j.conengprac.2005.11.002 · Cited by 52 · see citing works

Classification Techniques for Fault Detection and Diagnosis of an Air-Handling Unit | NIST

House, J. M., Lee, W., & Shin, D. R. (1999) · ASHRAE journal · Cited by 51 · see citing works

Data-driven based reliability evaluation for measurements of sensors in a vapor compression system

Du, Z., Chen, L., & Jin, X. (2017) · Energy · doi:10.1016/j.energy.2017.01.055 · Cited by 51 · see citing works

Comparative investigations on reference models for fault detection and diagnosis in centrifugal chiller systems

Tran, D. A. T., Chen, Y., & Jiang, C. (2016) · Energy and Buildings · doi:10.1016/j.enbuild.2016.09.062 · Cited by 49 · see citing works

A hybrid ICA-BPNN-based FDD strategy for refrigerant charge faults in variable refrigerant flow system

Sun, S., Li, G., Chen, H., et al. (2017) · Applied Thermal Engineering · doi:10.1016/j.applthermaleng.2017.08.047 · Cited by 48 · see citing works

Fault tolerant control of outdoor air and AHU supply air temperature in VAV air conditioning systems using PCA method

Jin, X., & Du, Z. (2005) · Applied Thermal Engineering · doi:10.1016/j.applthermaleng.2005.10.039 · Cited by 47 · see citing works

A machine learning bayesian network for refrigerant charge faults of variable refrigerant flow air conditioning system

Hu, M., Chen, H., Shen, L., et al. (2017) · Energy and Buildings · doi:10.1016/j.enbuild.2017.10.012 · Cited by 47 · see citing works

A robust fault detection and diagnosis strategy for pressure-independent VAV terminals of real office buildings

Wang, H., Chen, Y., Chan, C. W. H., et al. (2011) · Energy and Buildings · doi:10.1016/j.enbuild.2011.03.018 · Cited by 46 · see citing works

A robust model-based approach to diagnosing faults in air-handling units

Ngo, D., & Dexter, A. L. (1999) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · Cited by 45 · see citing works

Identification and isolation of outdoor fouling faults using only built-in sensors in variable refrigerant flow system: A data mining approach

Li, G., Hu, Y., Chen, H., Wang, J., Guo, Y., Liu, J., & Li, J. (2017) · Energy and Buildings · doi:10.1016/j.enbuild.2017.04.041 · Cited by 45 · see citing works

THE ENERGY IMPACT OF FAULTS IN U.S. COMMERCIAL BUILDINGS

Roth, K. W., Westphalen, D., Llana, P., et al. (2004) · Purdue e-Pubs (Purdue University System) · Cited by 44 · see citing works

Sensitivity analysis for PCA-based chiller sensor fault detection

Hu, Y., Li, G., Chen, H., et al. (2015) · International Journal of Refrigeration · doi:10.1016/j.ijrefrig.2015.11.006 · Cited by 44 · see citing works

THE 25TH IIR INTERNATIONAL CONGRESS OF REFRIGERATION

(2019) · International Journal of Refrigeration · doi:10.1016/j.ijrefrig.2019.02.037 · Cited by 44 · see citing works

Sensor Fault Detection and Diagnosis of Air-Handling Units Using a Condition-Based Adaptive Statistical Method

Wang, S., & Xiao, F. (2006) · Science and Technology for the Built Environment · doi:10.1080/10789669.2006.10391171 · Cited by 43 · see citing works

Decoupling features for fault detection and diagnosis on centrifugal chillers (1486-RP)

Zhao, X., Yang, M., & Li, H. (2011) · Science and Technology for the Built Environment · doi:10.1080/10789669.2011.543254 · Cited by 43 · see citing works

Valve fault detection and diagnosis based on CMAC neural networks

Wang, S., & Jiang, Z. (2004) · Energy and Buildings · doi:10.1016/j.enbuild.2004.01.037 · Cited by 42 · see citing works

Application of Control Charts for Detecting Faults in Variable-Air-Volume Boxes

Schein, J., & House, J. M. (2003) · ASHRAE journal · Cited by 42 · see citing works

An effective fault diagnosis method for centrifugal chillers using associative classification

Huang, R., Liu, J., Chen, H., et al. (2018) · Applied Thermal Engineering · doi:10.1016/j.applthermaleng.2018.03.041 · Cited by 40 · see citing works

A simplified physical model-based fault detection and diagnosis strategy and its customized tool for centrifugal chillers

Zhao, Y., Wang, S., Xiao, F., et al. (2013) · Science and Technology for the Built Environment · doi:10.1080/10789669.2013.765299 · Cited by 36 · see citing works

Automated fault detection and diagnosis of HVAC subsystems using statistical machine learning

West, S. R., Guo, Y., Wang, X. R., & Wall, J. (2011) · Cited by 36 · see citing works

Online Support Vector Machine Applicationfor Model Based Fault Detection and Isolationof HVAC System

Dehestani, D., Eftekhari, F., Guo, Y. J., et al. (2011) · International Journal of Machine Learning and Computing · doi:10.7763/ijmlc.2011.v1.10 · Cited by 36 · see citing works

Liquid floodback detection for scroll compressor in a VRF system under heating mode

Wang, J., Li, G., Chen, H., et al. (2016) · Applied Thermal Engineering · doi:10.1016/j.applthermaleng.2016.11.200 · Cited by 36 · see citing works

Detection and diagnosis for multiple faults in VAV systems

Du, Z., & Jin, X. (2006) · Energy and Buildings · doi:10.1016/j.enbuild.2006.09.015 · Cited by 35 · see citing works

PCA-FDA-Based Fault Diagnosis for Sensors in VAV Systems

Du, Z., Jin, X., & Wu, L. (2007) · Science and Technology for the Built Environment · doi:10.1080/10789669.2007.10390958 · Cited by 34 · see citing works

Modularized PCA method combined with expert-based multivariate decoupling for FDD in VRF systems including indoor unit faults

Guo, Y., Li, G., Chen, H., et al. (2017) · Applied Thermal Engineering · doi:10.1016/j.applthermaleng.2017.01.008 · Cited by 34 · see citing works

Mining big building operational data for improving building energy efficiency: A case study

Fan, C., & Xiao, F. (2017) · Building Services Engineering Research and Technology · doi:10.1177/0143624417704977 · Cited by 34 · see citing works

A development of easy-to-use tool for fault detection and diagnosis in building air-conditioning systems

Song, Y.-H., Akashi, Y., & Yee, J. (2007) · Energy and Buildings · doi:10.1016/j.enbuild.2007.01.011 · Cited by 33 · see citing works

Wavelet Neural Network-Based Fault Diagnosis in Air-Handling Units

Du, Z., Jin, X., & Yang, Y. (2008) · Science and Technology for the Built Environment · doi:10.1080/10789669.2008.10391049 · Cited by 33 · see citing works

Tolerant control for multiple faults of sensors in VAV systems

Du, Z., & Jin, X. (2006) · Energy Conversion and Management · doi:10.1016/j.enconman.2006.09.007 · Cited by 31 · see citing works

Automated Performance Tracking for Heat Exchangers in HVAC

Wang, P. E., & Gao, R. X. (2017) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2017.2666184 · Cited by 31 · see citing works

Fault diagnosis strategy for incompletely described samples and its application to refrigeration system

Ren, N., Liang, J. S., Gu, B. Q., et al. (2007) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2007.08.004 · Cited by 28 · see citing works

Fault-tolerant control and data recovery in HVAC monitoring system

Hao, X., Zhang, G., & Chen, Y. (2004) · Energy and Buildings · doi:10.1016/j.enbuild.2004.06.023 · Cited by 28 · see citing works

proceedings of the 19th Irish Conference on Artificial Intelligence and Cognitive Science (AICS 2008)

Tarim, Ş. A., Hnich, B., Rossi, R., et al. (2008) · Cited by 28 · see citing works

Building Fan Coil Electric Consumption Analysis with Fuzzy Approaches for Fault Detection and Diagnosis

Lauro, F., Moretti, F., Capozzoli, A., et al. (2014) · Energy Procedia · doi:10.1016/j.egypro.2014.12.403 · Cited by 26 · see citing works

Self-Correcting HVAC Controls Project Final Report

Fernandez, N., Brambley, M. R., Katipamula, S., et al. (2010) · doi:10.2172/971101 · Cited by 25 · see citing works

Fuzzy model-based condition monitoring and fault diagnosis of a diesel engine cooling system

Twiddle, J. A., & Jones, N. B. (2002) · Proceedings of the Institution of Mechanical Engineers Part I Journal of Systems and Control Engineering · doi:10.1177/095965180221600301 · Cited by 25 · see citing works

Are intelligent agents the key to optimizing building HVAC system performance?

Kelly, G. E., & Bushby, S. T. (2012) · Science and Technology for the Built Environment · doi:10.1080/10789669.2012.682693 · Cited by 24 · see citing works

Effect of the distribution of faults and operating conditions on AFDD performance evaluations

Yuill, D. P., & Braun, J. E. (2016) · Applied Thermal Engineering · doi:10.1016/j.applthermaleng.2016.06.149 · Cited by 23 · see citing works

2017 3rd IEEE International Conference on Control Science and Systems Engineering (ICCSSE)

(2017) · doi:10.1109/iccsse41979.2017 · Cited by 23 · see citing works

A system-level incipient fault-detection method for HVAC systems

Zhao, Y., Wang, S., & Xiao, F. (2013) · Science and Technology for the Built Environment · doi:10.1080/10789669.2013.789371 · Cited by 22 · see citing works

Results from simulation and laboratory testing of air handling unit and variable air volume box diagnostic tools

Milesi-Ferretti, N. S., Schein, J., Park, C. D., et al. (2003) · doi:10.1002/https://dx.doi.org/10.6028/nist.ir.6964 · Cited by 22 · see citing works

Application of Adaptive Resonance Theory neural networks to monitor solar hot water systems and detect existing or developing faults

He, H., Caudell, T. P., Menicucci, D., & Mammoli, A. A. (2012) · Solar Energy · doi:10.1016/j.solener.2012.05.015 · Cited by 21 · see citing works

A rule augmented statistical method for air-conditioning system fault detection and diagnostics

Li, Z., Paredis, C. J. J., Augenbroe, G., et al. (2012) · Energy and Buildings · doi:10.1016/j.enbuild.2012.08.001 · Cited by 20 · see citing works

Monte Carlo analysis of the effect of uncertainties on model-based HVAC fault detection and diagnostics

Wang, L., & Haves, P. (2014) · Science and Technology for the Built Environment · doi:10.1080/10789669.2014.924354 · Cited by 16 · see citing works

Estimation of an incipient fault using an adaptive neurofuzzy sliding-mode observer

Zhou, Y., Liu, J., & Dexter, A. L. (2014) · Energy and Buildings · doi:10.1016/j.enbuild.2014.02.001 · Cited by 16 · see citing works

Research on the PCA-based Intelligent Fault Detection Methodology for Sewage Source Heat Pump System

Yu, D., Yu, J., Sun, F., et al. (2017) · Procedia Engineering · doi:10.1016/j.proeng.2017.10.171 · Cited by 16 · see citing works

Results from field testing of air handling unit and variable air volume box fault detection tools

Schein, J., Bushby, S. T., Castro, N. S., et al. (2003) · doi:10.6028/nist.ir.6994 · Cited by 16 · see citing works

A Semi-automated Commissioning Tool for VAV Air Handling Units:Functional Test Analyzer

Haves, P., Kim, M., Najafi, M., et al. (2007) · ASHRAE winter conference papers · Cited by 15 · see citing works

Distributed adaptive sensor fault tolerant control for smart buildings

Papadopoulos, P., Reppa, V., Polycarpou, M. M., et al. (2015) · Proceedings of the IEEE Conference on Decision & Control, including the Symposium on Adaptive Processes · doi:10.1109/cdc.2015.7402690 · Cited by 15 · see citing works

Distributed Diagnosis of Actuator and Sensor Faults in HVAC Systems

Papadopoulos, P., Reppa, V., Polycarpou, M. M., et al. (2017) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2017.08.816 · Cited by 15 · see citing works

Results from Field Testing of Air Handling Unit and Variable Air Volume Box Fault Detection Tools

Schein, J., Bushby, S. T., Milesi-Ferretti, N. S., et al. (2003) · doi:10.1002/https://dx.doi.org/10.6028/nist.ir.6994 · Cited by 15 · see citing works

Robust fault tolerant application for HVAC system based on combination of online SVM and ANN black box model

Dehestani, D., Su, S. W., Nguyen, H. T., et al. (2013) · European Control Conference (ECC) · doi:10.23919/ecc.2013.6669140 · Cited by 14 · see citing works

Real-Time Fault Detection for Solar Hot Water Systems Using Adaptive Resonance Theory Neural Networks

He, H., Menicucci, D., Caudell, T. P., et al. (2011) · ASME International Conference on Energy Sustainability (ES) · doi:10.1115/es2011-54885 · Cited by 14 · see citing works

Energy Efficiency in Buildings in EU Countries

Friedrich, S. (2013) · RePEc: Research Papers in Economics · Cited by 13 · see citing works

Automatic commissioning of air-conditioning plant

Ngo, D. (1998) · UKACC International Conference on Control · doi:10.1049/cp:19980484 · Cited by 11 · see citing works

Fault detection and diagnosis in liquid chillers

Rueda, E., Tassou, S. A., & Grace, I. N. (2005) · Proceedings of the Institution of Mechanical Engineers Part E Journal of Process Mechanical Engineering · doi:10.1243/095440805x8575 · Cited by 11 · see citing works

Energy Efficiency in Buildings in EU Countries

SilkeFriedrich (2013) · RePEc: Research Papers in Economics · Cited by 11 · see citing works

Heat Recovery Unit Failure Detection in Air Handling Unit

Madhikermi, M., Yousefnezhad, N., & Främling, K. (2018) · IFIP advances in information and communication technology · doi:10.1007/978-3-319-99707-0_43 · Cited by 10 · see citing works

Fusing system configuration information for building cooling plant Fault Detection and severity level identification

Li, D., Zhou, Y., Hu, G., et al. (2016) · 2022 IEEE 18th International Conference on Automation Science and Engineering (CASE) · doi:10.1109/coase.2016.7743561 · Cited by 9 · see citing works

On-Line Monitoring and Fault Detection of Control System Performance | NIST

Seem, J. E., House, J. M., & Monroe, R. H. (1999) · ASHRAE journal · Cited by 8 · see citing works

Fault detection and diagnostics of an HVAC sub-system using adaptive resonance theory neural networks

Jones, C. B. (2015) · UNM’s Digital Repository (University of New Mexico) · Cited by 8 · see citing works

Whole building system fault detection based on weather pattern matching and PCA method

Chen, Y., & Wen, J. (2017) · IEEE International Conference on Control Science and Systems Engineering (ICCSSE) · doi:10.1109/ccsse.2017.8088030 · Cited by 7 · see citing works

Fault tolerant control strategies for a high-rise building hot water heating system

Li, L., & Zaheeruddin, M. (2014) · Building Services Engineering Research and Technology · doi:10.1177/0143624414531588 · Cited by 6 · see citing works

Fuzzy model-based condition monitoring and fault diagnosis of a diesel engine cooling system

Twiddle, J. A., & Jones, N. B. (2002) · Proceedings of the Institution of Mechanical Engineers Part I Journal of Systems and Control Engineering · doi:10.1243/095965102320005292 · Cited by 6 · see citing works

Development of Economic Impact Models for RTU Economizer Faults

Hjortland, A. L., & Braun, J. E. (2014) · Purdue e-Pubs (Purdue University System) · Cited by 4 · see citing works

Probabilistic fault detection and diagnostics for packaged air-conditioner outdoor-air economizers

Hjortland, A. L. (2014) · Purdue e-Pubs (Purdue University System) · Cited by 3 · see citing works

Automated performance tracking for heat exchangers in HVAC

Wang, P., Tang, X., & Gao, R. X. (2015) · 2022 IEEE 18th International Conference on Automation Science and Engineering (CASE) · doi:10.1109/coase.2015.7294221 · Cited by 3 · see citing works

Study on the support vector data description (SVDD)-based chiller sensor fault detection efficiencies.

Li, G., Hu, Y., Chen, H., et al. (2015) · Institut International du Froid · doi:10.18462/iir.icr.2015.0294 · Cited by 3 · see citing works

Field Testing and Demonstration of the Smart Monitoring and Diagnostic System (SMDS) for Packaged Air-Conditioners and Heat Pumps

Taasevigen, D. J., USDOE Office of Energy Efficiency and Renewable Energy (EERE), Brambley, M. R., et al. (2015) · doi:10.2172/1194304 · Cited by 2 · see citing works

A novel fault detection strategy for centrifugal chiller based on support vector data description (SVDD)

Zhao, Y., Wang, S., & Xiao, F. (2012) · PolyU Institutional Research Archive (Hong Kong Polytechnic University) · Cited by 1 · see citing works

Artificial intelligence in sustainable energy industry: Status Quo, challenges and opportunities

Ahmad, T., Zhang, D., Huang, C., Zhang, H., Dai, N., Song, Y., & Chen, H. (2021) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2021.125834 · Cited by 1,258 · see citing works

Key technologies for smart energy systems: Recent developments, challenges, and research opportunities in the context of carbon neutrality

Zhu, H., Goh, H. H., Zhang, D., Ahmad, T., Liu, H., Wang, S., Li, S., Liu, T., Dai, H., & Wu, T. (2022) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2021.129809 · Cited by 131 · see citing works

A hybrid deep learning-based method for short-term building energy load prediction combined with an interpretation process

Zhang, C., Li, J., Zhao, Y., Li, T., Chen, Q., & Zhang, X. (2020) · Energy and Buildings · doi:10.1016/j.enbuild.2020.110301 · Cited by 124 · see citing works

A Review of the Fault Behavior of Heat Pumps and Measurements, Detection and Diagnosis Methods Including Virtual Sensors

Bellanco, I., Fuentes, E., Vallès, M., & Salom, J. (2021) · Journal of Building Engineering · doi:10.1016/j.jobe.2021.102254 · Cited by 84 · see citing works

A generic prediction interval estimation method for quantifying the uncertainties in ultra-short-term building cooling load prediction

Zhang, C., Zhao, Y., Fan, C., Li, T., Zhang, X., & Li, J. (2020) · Applied Thermal Engineering · doi:10.1016/j.applthermaleng.2020.115261 · Cited by 79 · see citing works

Selection of the Informative Frequency Band in a Bearing Fault Diagnosis in the Presence of Non-Gaussian Noise—Comparison of Recently Developed Methods

Hebda-Sobkowicz, J., Zimroz, R., & Wyłomańska, A. (2020) · Applied Sciences · doi:10.3390/app10082657 · Cited by 65 · see citing works

Artificial intelligence integrated grid systems: Technologies, potential frameworks, challenges, and research directions

Alam, M. M., Hossain, M. J., Habib, M. A., Arafat, M. Y., & Hannan, M. A. (2025) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2024.115251 · Cited by 76 · see citing works

AI-driven design optimization for sustainable buildings: a systematic review

Manmatharasan, P., Bitsuamlak, G., & Grolinger, K. (2025) · Energy and Buildings · doi:10.1016/j.enbuild.2025.115440 · Cited by 63 · see citing works

Utilization and challenges of artificial intelligence in the energy sector

Park, C., & Kim, M. (2026) · Energy & Environment · doi:10.1177/0958305x241258795 · Cited by 15 · see citing works

Design and Implementation of Agricultural Expert System Tool

Gui, Z. (1999) · Nongye gongcheng xuebao · Cited by 1 · see citing works

EXPERT SYSTEMS IN AGRICULTURE

Journal, A. (1990) · Shanghai nongye xuebao · Cited by 10 · see citing works

Expert System: A Tool for Expert Decision

Chakrabarty, B. K. (2002) · Transactions of the Indian Ceramic Society · doi:10.1080/0371750x.2002.10800044 · Cited by 2 · see citing works

Expert Systems

Dutta, A., & Bhattacharyya, A. (2006) · IETE Journal of Education · doi:10.1080/09747338.2006.11415860 · Cited by 1 · see citing works

SISTEM PAKAR UNTUK MENDIAGNOSA PENYAKIT INFEKSI TBC PARU

Perwira, R. I. (2014) · Telematika · doi:10.31315/telematika.v9i2.285 · Cited by 4 · see citing works