Babadi Soultanzadeh, Milad (2024) Automated Fault Detection and Diagnosis in Light Commercial Building’s HVAC systems. Masters thesis, Concordia University.
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Abstract
Fault detection and diagnosis (FDD) in commercial buildings' HVAC systems can significantly reduce energy consumption. Faults in these systems occur due to various issues such as aging and inadequate maintenance. Commercial buildings in Canada covered an area of 709,029,612 m² in 2019, consuming 948,216,746 GJ of energy. HVAC systems are responsible for 25%-50% of this energy consumption. In the United States, faults in HVAC systems contribute to an additional energy consumption of 103 to 500 terawatt-hours (TWh) in the building sector. Detecting and diagnosing faults in HVAC systems can reduce energy consumption by 20% to 30%. Light commercial buildings, defined as commercial buildings with fewer than six stories and less than 2500 square feet, include bank branches, offices, and small industrial facilities. These buildings have similarities in the configuration and size of HVAC components, making it feasible to develop an FDD tailored for this class of buildings, that can be easily scaled up. This goal can be achieved using data-driven methods, which have gained popularity over the past decades by installing various sensors and collecting data integrated with Building Energy Management (BEM) systems. In this thesis, three different FDD methods have been developed and validated on light commercial buildings. The first method is a semi-supervised method that includes various techniques to handle the unlabeled raw data from BEMs, resulting in a final supervised Automatic Fault Detection (AFDD) system. The second method is a fully unsupervised novel AFDD method based on PCA time series fault detection. The third method is primarily based on the inverse model of the Air Handling Unit (AHU) of the HVAC systems. A typical light commercial building in Montreal, Canada, was used for all methods. Additionally, to validate the generalizability of the unsupervised method, another light commercial building, a small industrial facility in Ireland, was used as well. The first method successfully resulted in an AFDD that can detect and diagnose faults with almost 90% accuracy, performing better in condition-based faults than control faults. The unsupervised method showed very good results in terms of generalizability. It was able to detect faults and report the problematic inputs and locations to the HVAC operators. Although the unsupervised method cannot completely diagnose condition-based faults, it provides very good information based on the system's behavior, enabling operators to diagnose the faults. Finally, the inverse modeling revealed that a physics-based neural network can outperform neural networks and genetic algorithms in modeling the system inversely and detecting anomalies mostly related to energy consumption.
| Divisions: | Concordia University > Gina Cody School of Engineering and Computer Science > Building, Civil and Environmental Engineering |
|---|---|
| Item Type: | Thesis (Masters) |
| Authors: | Babadi Soultanzadeh, Milad |
| Institution: | Concordia University |
| Degree Name: | M.A. Sc. |
| Program: | Building Engineering |
| Date: | 5 August 2024 |
| Thesis Supervisor(s): | Ouf, Mohamed and Nik-Bakht, Mazdak |
| Keywords: | Automated fault detection and diagnostics, HVAC, Machine Learning, Light commercial buildings |
| ID Code: | 994460 |
| Deposited By: | Milad babadi Soultanzadeh |
| Deposited On: | 24 Oct 2024 15:38 |
| Last Modified: | 24 Oct 2024 15:38 |
References:
[1] F. Xiao, S. Wang, Progress and methodologies of lifecycle commissioning of HVAC systems to enhance building sustainability, Renewable and Sustainable Energy Reviews 13 (2009) 1144–1149. https://doi.org/10.1016/j.rser.2008.03.006.[2] M.S. Mirnaghi, F. Haghighat, Fault detection and diagnosis of large-scale HVAC systems in buildings using data-driven methods: A comprehensive review, Energy Build 229 (2020). https://doi.org/10.1016/j.enbuild.2020.110492.
[3] M.S. Piscitelli, D.M. Mazzarelli, A. Capozzoli, Enhancing operational performance of AHUs through an advanced fault detection and diagnosis process based on temporal association and decision rules, Energy Build 226 (2020). https://doi.org/10.1016/j.enbuild.2020.110369.
[4] D. Dey, B. Dong, A probabilistic approach to diagnose faults of air handling units in buildings, Energy Build 130 (2016) 177–187. https://doi.org/10.1016/j.enbuild.2016.08.017.
[5] S. Frank, G. Lin, X. Jin, R. Singla, A. Farthing, J. Granderson, A performance evaluation framework for building fault detection and diagnosis algorithms, Energy Build 192 (2019) 84–92. https://doi.org/10.1016/j.enbuild.2019.03.024.
[6] A. Abid, M.T. Khan, J. Iqbal, A review on fault detection and diagnosis techniques: basics and beyond, Artif Intell Rev 54 (2021) 3639–3664. https://doi.org/10.1007/s10462-020-09934-2.
[7] B. Gunay, J. Bursill, B. Huchuk, S. Shillinglaw, Inverse model-based detection of programming logic faults in multiple zone VAV AHU systems, Build Environ 211 (2022). https://doi.org/10.1016/j.buildenv.2021.108732.
[8] H.B. Gunay, Z. Shi, G. Newsham, R. Moromisato, Detection of zone sensor and actuator faults through inverse greybox modelling, Build Environ 171 (2020). https://doi.org/10.1016/j.buildenv.2020.106659.
[9] Y. Yu, D. Woradechjumroen, D. Yu, A review of fault detection and diagnosis methodologies on air-handling units, Energy Build 82 (2014) 550–562. https://doi.org/10.1016/j.enbuild.2014.06.042.
[10] N. Torabi, H. Burak Gunay, W. O’Brien, R. Moromisato, Inverse model-based virtual sensors for detection of hard faults in air handling units, Energy Build 253 (2021). https://doi.org/10.1016/j.enbuild.2021.111493.
[11] A. Hosseini Gourabpasi, M. Nik-Bakht, Knowledge Discovery by Analyzing the State of the Art of Data-Driven Fault Detection and Diagnostics of Building HVAC, CivilEng 2 (2021) 986–1008. https://doi.org/10.3390/civileng2040053.
[12] W. Kim, S. Katipamula, A review of fault detection and diagnostics methods for building systems, Sci Technol Built Environ 24 (2018) 3–21. https://doi.org/10.1080/23744731.2017.1318008.
[13] A. Beghi, R. Brignoli, L. Cecchinato, G. Menegazzo, M. Rampazzo, F. Simmini, Data-driven Fault Detection and Diagnosis for HVAC water chillers, Control Eng Pract 53 (2016) 79–91. https://doi.org/10.1016/j.conengprac.2016.04.018.
[14] Z. Chen, Z. O’Neill, J. Wen, O. Pradhan, T. Yang, X. Lu, G. Lin, S. Miyata, S. Lee, C. Shen, R. Chiosa, M.S. Piscitelli, A. Capozzoli, F. Hengel, A. Kührer, M. Pritoni, W. Liu, J. Clauß, Y. Chen, T. Herr, A review of data-driven fault detection and diagnostics for building HVAC systems, Appl Energy 339 (2023). https://doi.org/10.1016/j.apenergy.2023.121030.
[15] S. Katipamula, M.R. Brambley, Review article: Methods for fault detection, diagnostics, and prognostics for building systems—A review, part I, HVAC and R Research 11 (2005) 3–25. https://doi.org/10.1080/10789669.2005.10391123.
[16] S.P. Melgaard, K.H. Andersen, A. Marszal-Pomianowska, R.L. Jensen, P.K. Heiselberg, Fault Detection and Diagnosis Encyclopedia for Building Systems: A Systematic Review, Energies (Basel) 15 (2022). https://doi.org/10.3390/en15124366.
[17] Ph.D.; S.L.Ph.D. Jin Wen, RP-1312 -- TOOLS FOR EVALUATING FAULT DETECTION AND DIAGNOSTIC METHODS FOR AIR-HANDLING UNITS, 2012.
[18] Jessica Granderson, Guanjing Lin, Yimin Chen, Armando Casillas, Sen Huang, Draguna Vrabie, LBNL Fault Detection and Diagnostics Data Sets: Single Duct Air Handling Unit, 2022.
[19] M. Ahern, D.T.J. O’sullivan, K. Bruton, Development of a Framework to Aid the Transition from Reactive to Proactive Maintenance Approaches to Enable Energy Reduction, Applied Sciences (Switzerland) 12 (2022). https://doi.org/10.3390/app12136704.
[20] J. Huang, J. Wen, H. Yoon, O. Pradhan, T. Wu, Z. O’Neill, K. Selcuk Candan, Real vs. simulated: Questions on the capability of simulated datasets on building fault detection for energy efficiency from a data-driven perspective, Energy Build 259 (2022). https://doi.org/10.1016/j.enbuild.2022.111872.
[21] Z. Du, B. Fan, X. Jin, J. Chi, Fault detection and diagnosis for buildings and HVAC systems using combined neural networks and subtractive clustering analysis, Build Environ 73 (2014) 1–11. https://doi.org/10.1016/j.buildenv.2013.11.021.
[22] C. Miller, Z. Nagy, A. Schlueter, Automated daily pattern filtering of measured building performance data, Autom Constr 49 (2015) 1–17. https://doi.org/10.1016/j.autcon.2014.09.004.
[23] D. Li, G. Hu, C.J. Spanos, A data-driven strategy for detection and diagnosis of building chiller faults using linear discriminant analysis, Energy Build 128 (2016) 519–529. https://doi.org/10.1016/j.enbuild.2016.07.014.
[24] G. Li, Y. Hu, Improved sensor fault detection, diagnosis and estimation for screw chillers using density-based clustering and principal component analysis, Energy Build 173 (2018) 502–515. https://doi.org/10.1016/j.enbuild.2018.05.025.
[25] C. Fan, F. Xiao, C. Yan, A framework for knowledge discovery in massive building automation data and its application in building diagnostics, Autom Constr 50 (2015) 81–90. https://doi.org/10.1016/j.autcon.2014.12.006.
[26] M. Dey, S.P. Rana, S. Dudley, A case study based approach for remote fault detection using multi-level machine learning in a smart building, Smart Cities 3 (2020) 401–419. https://doi.org/10.3390/smartcities3020021.
[27] M. Dey, S.P. Rana, S. Dudley, Smart building creation in large scale HVAC environments through automated fault detection and diagnosis, Future Generation Computer Systems 108 (2020) 950–966. https://doi.org/10.1016/j.future.2018.02.019.
[28] H.B. Gunay, Z. Shi, Cluster analysis-based anomaly detection in building automation systems, Energy Build 228 (2020). https://doi.org/10.1016/j.enbuild.2020.110445.
[29] J. Aguilar, D. Ardila, A. Avendaño, F. Macias, C. White, J. Gomez-Pulido, J.G. De Mesa, A. Garces-Jimenez, An autonomic cycle of data analysis tasks for the supervision of HVAC systems of smart building, Energies (Basel) 13 (2020). https://doi.org/10.3390/en13123103.
[30] Y. Xu, C. Yan, J. Shi, Z. Lu, X. Niu, Y. Jiang, F. Zhu, An anomaly detection and dynamic energy performance evaluation method for HVAC systems based on data mining, Sustainable Energy Technologies and Assessments 44 (2021). https://doi.org/10.1016/j.seta.2021.101092.
[31] A. Rosato, M.S. Piscitelli, A. Capozzoli, Data-Driven Fault Detection and Diagnosis: Research and Applications for HVAC Systems in Buildings, Energies (Basel) 16 (2023). https://doi.org/10.3390/en16020854.
[32] R. Yan, Z. Ma, Y. Zhao, G. Kokogiannakis, A decision tree based data-driven diagnostic strategy for air handling units, Energy Build 133 (2016) 37–45. https://doi.org/10.1016/j.enbuild.2016.09.039.
[33] G. Li, H. Chen, Y. Hu, J. Wang, Y. Guo, J. Liu, H. Li, R. Huang, H. Lv, J. Li, An improved decision tree-based fault diagnosis method for practical variable refrigerant flow system using virtual sensor-based fault indicators, Appl Therm Eng 129 (2018) 1292–1303. https://doi.org/10.1016/j.applthermaleng.2017.10.013.
[34] A. Capozzoli, M.S. Piscitelli, S. Brandi, D. Grassi, G. Chicco, Automated load pattern learning and anomaly detection for enhancing energy management in smart buildings, Energy 157 (2018) 336–352. https://doi.org/10.1016/j.energy.2018.05.127.
[35] M.S. Piscitelli, S. Brandi, A. Capozzoli, F. Xiao, A data analytics-based tool for the detection and diagnosis of anomalous daily energy patterns in buildings, Build Simul 14 (2021) 131–147. https://doi.org/10.1007/s12273-020-0650-1.
[36] X. Liu, Y. Ding, H. Tang, F. Xiao, A data mining-based framework for the identification of daily electricity usage patterns and anomaly detection in building electricity consumption data, Energy Build 231 (2021). https://doi.org/10.1016/j.enbuild.2020.110601.
[37] R. Chiosa, M.S. Piscitelli, A. Capozzoli, A data analytics-based energy information system (EIS) tool to perform meter-level anomaly detection and diagnosis in buildings, Energies (Basel) 14 (2021). https://doi.org/10.3390/en14010237.
[38] R. Chiosa, M.S. Piscitelli, C. Fan, A. Capozzoli, Towards a self-tuned data analytics-based process for an automatic context-aware detection and diagnosis of anomalies in building energy consumption timeseries, Energy Build 270 (2022). https://doi.org/10.1016/j.enbuild.2022.112302.
[39] S. Wang, J. Qin, Sensor fault detection and validation of VAV terminals in air conditioning systems, Energy Convers Manag 46 (2005) 2482–2500. https://doi.org/10.1016/j.enconman.2004.11.011.
[40] Z. Du, X. Jin, Detection and diagnosis for sensor fault in HVAC systems, Energy Convers Manag 48 (2007) 693–702. https://doi.org/10.1016/j.enconman.2006.09.023.
[41] Z. Du, X. Jin, Multiple faults diagnosis for sensors in air handling unit using Fisher discriminant analysis, Energy Convers Manag 49 (2008) 3654–3665. https://doi.org/10.1016/j.enconman.2008.06.032.
[42] Y. Hu, H. Chen, J. Xie, X. Yang, C. Zhou, Chiller sensor fault detection using a self-Adaptive Principal Component Analysis method, Energy Build 54 (2012) 252–258. https://doi.org/10.1016/j.enbuild.2012.07.014.
[43] S. Li, J. Wen, A model-based fault detection and diagnostic methodology based on PCA method and wavelet transform, Energy Build 68 (2014) 63–71. https://doi.org/10.1016/j.enbuild.2013.08.044.
[44] S. Li, J. Wen, Application of pattern matching method for detecting faults in air handling unit system, Autom Constr 43 (2014) 49–58. https://doi.org/10.1016/j.autcon.2014.03.002.
[45] N. Cotrufo, R. Zmeureanu, PCA-based method of soft fault detection and identification for the ongoing commissioning of chillers, Energy Build 130 (2016) 443–452. https://doi.org/10.1016/j.enbuild.2016.08.083.
[46] Y. Hu, H. Chen, G. Li, H. Li, R. Xu, J. Li, A statistical training data cleaning strategy for the PCA-based chiller sensor fault detection, diagnosis and data reconstruction method, Energy Build 112 (2016) 270–278. https://doi.org/10.1016/j.enbuild.2015.11.066.
[47] Y. Guo, G. Li, H. Chen, Y. Hu, H. Li, L. Xing, W. Hu, An enhanced PCA method with Savitzky-Golay method for VRF system sensor fault detection and diagnosis, Energy Build 142 (2017) 167–178. https://doi.org/10.1016/j.enbuild.2017.03.026.
[48] S. Shi, G. Li, H. Chen, Y. Hu, X. Wang, Y. Guo, S. Sun, An efficient VRF system fault diagnosis strategy for refrigerant charge amount based on PCA and dual neural network model, Appl Therm Eng 129 (2018) 1252–1262. https://doi.org/10.1016/j.applthermaleng.2017.09.117.
[49] L. Burgas, J. Colomer, J. Melendez, F.I. Gamero, S. Herraiz, Integrated unfold-pca monitoring application for smart buildings: An ahu application example, Energies (Basel) 14 (2021). https://doi.org/10.3390/en14010235.
[50] Z. Zhou, H. Chen, G. Li, H. Zhong, M. Zhang, J. Wu, Data-driven fault diagnosis for residential variable refrigerant flow system on imbalanced data environments, International Journal of Refrigeration 125 (2021) 34–43. https://doi.org/10.1016/j.ijrefrig.2021.01.009.
[51] M. Dey, S.P. Rana, S. Dudley, Smart building creation in large scale HVAC environments through automated fault detection and diagnosis, Future Generation Computer Systems 108 (2020) 950–966. https://doi.org/10.1016/j.future.2018.02.019.
[52] A. Alghanmi, A. Yunusa-Kaltungo, A whole-building data-driven fault detection and diagnosis approach for public buildings in hot climate regions, Energy and Built Environment (2023). https://doi.org/10.1016/j.enbenv.2023.07.005.
[53] A. Liang, Y. Hu, G. Li, The impact of improved PCA method based on anomaly detection on chiller sensor fault detection, International Journal of Refrigeration 155 (2023) 184–194. https://doi.org/10.1016/j.ijrefrig.2023.09.002.
[54] T. Zhao, B. Zhang, M. Li, G. Liu, P. Wang, Handling fault detection and diagnosis in incomplete sensor measurements for BAS based HVAC system, Journal of Building Engineering 80 (2023). https://doi.org/10.1016/j.jobe.2023.108098.
[55] X. Yang, J. Chen, X. Gu, R. He, J. Wang, Sensitivity analysis of scalable data on three PCA related fault detection methods considering data window and thermal load matching strategies, Expert Syst Appl 234 (2023). https://doi.org/10.1016/j.eswa.2023.121024.
[56] C. Fan, Q. Wu, Y. Zhao, L. Mo, Integrating active learning and semi-supervised learning for improved data-driven HVAC fault diagnosis performance, Appl Energy 356 (2024). https://doi.org/10.1016/j.apenergy.2023.122356.
[57] N. Torabi, H. Burak Gunay, W. O’Brien, R. Moromisato, Inverse model-based virtual sensors for detection of hard faults in air handling units, Energy Build 253 (2021). https://doi.org/10.1016/j.enbuild.2021.111493.
[58] D. Darwazeh, B. Gunay, J. Duquette, Development of Inverse Greybox Model-Based Virtual Meters for Air Handling Units, IEEE Transactions on Automation Science and Engineering 18 (2021) 323–336. https://doi.org/10.1109/TASE.2020.3005888.
[59] B. Gunay, J. Bursill, B. Huchuk, S. Shillinglaw, Inverse model-based detection of programming logic faults in multiple zone VAV AHU systems, Build Environ 211 (2022). https://doi.org/10.1016/j.buildenv.2021.108732.
[60] S. Li, J. Wen, A model-based fault detection and diagnostic methodology based on PCA method and wavelet transform, Energy Build 68 (2014) 63–71. https://doi.org/10.1016/j.enbuild.2013.08.044.
[61] J.Edward. Jackson, A user’s guide to principal components, Wiley, 1991.
[62] Y. Hu, H. Chen, J. Xie, X. Yang, C. Zhou, Chiller sensor fault detection using a self-Adaptive Principal Component Analysis method, Energy Build 54 (2012) 252–258. https://doi.org/10.1016/j.enbuild.2012.07.014.
[63] S. Wang, F. Xiao, Detection and diagnosis of AHU sensor faults using principal component analysis method, Energy Convers Manag 45 (2004) 2667–2686. https://doi.org/10.1016/j.enconman.2003.12.008.
[64] S. Wang, F. Xiao, AHU sensor fault diagnosis using principal component analysis method, Energy Build 36 (2004) 147–160. https://doi.org/10.1016/j.enbuild.2003.10.002.
[65] S. Wang, J. Qin, Sensor fault detection and validation of VAV terminals in air conditioning systems, Energy Convers Manag 46 (2005) 2482–2500. https://doi.org/10.1016/j.enconman.2004.11.011.
[66] S. Wang, J. Cui, A robust fault detection and diagnosis strategy for centrifugal chillers, HVAC and R Research 12 (2006) 407–428. https://doi.org/10.1080/10789669.2006.10391187.
[67] S. Wang, F. Xiao, Sensor fault detection and diagnosis of air-handling units using a condition-based adaptive statistical method, HVAC and R Research 12 (2006) 127–150. https://doi.org/10.1080/10789669.2006.10391171.
[68] F. Xiao, S. Wang, J. Zhang, A diagnostic tool for online sensor health monitoring in air-conditioning systems, Autom Constr 15 (2006) 489–503. https://doi.org/10.1016/j.autcon.2005.06.001.
[69] Z. Du, X. Jin, Detection and diagnosis for sensor fault in HVAC systems, Energy Convers Manag 48 (2007) 693–702. https://doi.org/10.1016/j.enconman.2006.09.023.
[70] Z. Du, X. Jin, Multiple faults diagnosis for sensors in air handling unit using Fisher discriminant analysis, Energy Convers Manag 49 (2008) 3654–3665. https://doi.org/10.1016/j.enconman.2008.06.032.
[71] F. Xiao, S. Wang, X. Xu, G. Ge, An isolation enhanced PCA method with expert-based multivariate decoupling for sensor FDD in air-conditioning systems, Appl Therm Eng 29 (2009) 712–722. https://doi.org/10.1016/j.applthermaleng.2008.03.046.
[72] S. Wang, Q. Zhou, F. Xiao, A system-level fault detection and diagnosis strategy for HVAC systems involving sensor faults, Energy Build 42 (2010) 477–490. https://doi.org/10.1016/j.enbuild.2009.10.017.
[73] S. Li, J. Wen, Application of pattern matching method for detecting faults in air handling unit system, Autom Constr 43 (2014) 49–58. https://doi.org/10.1016/j.autcon.2014.03.002.
[74] M. Padilla, D. Choinière, A combined passive-active sensor fault detection and isolation approach for air handling units, Energy Build 99 (2015) 214–219. https://doi.org/10.1016/j.enbuild.2015.04.035.
[75] R. Yan, Z. Ma, G. Kokogiannakis, Y. Zhao, A sensor fault detection strategy for air handling units using cluster analysis, Autom Constr 70 (2016) 77–88. https://doi.org/10.1016/j.autcon.2016.06.005.
[76] Y. Hu, H. Chen, G. Li, H. Li, R. Xu, J. Li, A statistical training data cleaning strategy for the PCA-based chiller sensor fault detection, diagnosis and data reconstruction method, Energy Build 112 (2016) 270–278. https://doi.org/10.1016/j.enbuild.2015.11.066.
[77] Y. Guo, G. Li, H. Chen, Y. Hu, H. Li, L. Xing, W. Hu, An enhanced PCA method with Savitzky-Golay method for VRF system sensor fault detection and diagnosis, Energy Build 142 (2017) 167–178. https://doi.org/10.1016/j.enbuild.2017.03.026.
[78] Y. Guo, G. Li, H. Chen, Y. Hu, H. Li, J. Liu, M. Hu, W. Hu, Modularized PCA method combined with expert-based multivariate decoupling for FDD in VRF systems including indoor unit faults, Appl Therm Eng 115 (2017) 744–755. https://doi.org/10.1016/j.applthermaleng.2017.01.008.
[79] G. Li, Y. Hu, Improved sensor fault detection, diagnosis and estimation for screw chillers using density-based clustering and principal component analysis, Energy Build 173 (2018) 502–515. https://doi.org/10.1016/j.enbuild.2018.05.025.
[80] A. Montazeri, S.M. Kargar, Fault detection and diagnosis in air handling using data-driven methods, Journal of Building Engineering 31 (2020). https://doi.org/10.1016/j.jobe.2020.101388.
[81] Y. Guo, H. Chen, Fault diagnosis of VRF air-conditioning system based on improved Gaussian mixture model with PCA approach, International Journal of Refrigeration 118 (2020) 1–11. https://doi.org/10.1016/j.ijrefrig.2020.06.009.
[82] L. Burgas, J. Colomer, J. Melendez, F.I. Gamero, S. Herraiz, Integrated unfold-pca monitoring application for smart buildings: An ahu application example, Energies (Basel) 14 (2021). https://doi.org/10.3390/en14010235.
[83] X. Yang, R. He, J. Wang, X. Li, R. Liu, Using thermal load matching strategy to locate historical benchmark data for moving-window PCA based fault detection in air handling units, Sustainable Energy Technologies and Assessments 52 (2022). https://doi.org/10.1016/j.seta.2022.102238.
[84] A. Liang, Y. Hu, G. Li, The impact of improved PCA method based on anomaly detection on chiller sensor fault detection, International Journal of Refrigeration 155 (2023) 184–194. https://doi.org/10.1016/j.ijrefrig.2023.09.002.
[85] S. Wen, W. Zhang, Y. Sun, Z. Li, B. Huang, S. Bian, L. Zhao, Y. Wang, An enhanced principal component analysis method with Savitzky–Golay filter and clustering algorithm for sensor fault detection and diagnosis, Appl Energy 337 (2023). https://doi.org/10.1016/j.apenergy.2023.120862.
[86] X. Yang, J. Chen, X. Gu, R. He, J. Wang, Sensitivity analysis of scalable data on three PCA related fault detection methods considering data window and thermal load matching strategies, Expert Syst Appl 234 (2023). https://doi.org/10.1016/j.eswa.2023.121024.
[87] Q. Ma, C. Yue, M. Yu, Y. Song, P. Cui, Y. Yu, Research on fault diagnosis strategy of air-conditioning system based on signal demodulation and BPNN-PCA, International Journal of Refrigeration 158 (2024) 124–134. https://doi.org/10.1016/j.ijrefrig.2023.12.008.
[88] G. Li, C. Xiong, J. Gao, H. Zhu, C. Wang, J. Xiao, Fault detection, diagnosis and calibration of heating, ventilation and air conditioning sensors by combining principal component analysis and improved bayesian inference, Journal of Building Engineering 82 (2024) 108230. https://doi.org/10.1016/j.jobe.2023.108230.
[89] Mathew C. Comstock, James E. Braun, RP-1043 -- Fault Detection And Diagnostic (FDD) Requirements And Evaluation Tools For Chillers, 2006.
[90] K. Bruton, D. Coakley, P. Raftery, D.O. Cusack, M.M. Keane, D.T.J. O’Sullivan, Comparative analysis of the AHU InFO fault detection and diagnostic expert tool for AHUs with APAR, Energy Effic 8 (2015) 299–322. https://doi.org/10.1007/s12053-014-9289-z.
[91] K. Bruton, P. Raftery, B. Kennedy, M.M. Keane, D.T.J. O’Sullivan, Review of automated fault detection and diagnostic tools in air handling units, Energy Effic 7 (2014) 335–351. https://doi.org/10.1007/s12053-013-9238-2.
[92] Z. Shi, W. O’Brien, Development and implementation of automated fault detection and diagnostics for building systems: A review, Autom Constr 104 (2019) 215–229. https://doi.org/10.1016/j.autcon.2019.04.002.
[93] Y. Zhao, T. Li, X. Zhang, C. Zhang, Artificial intelligence-based fault detection and diagnosis methods for building energy systems: Advantages, challenges and the future, Renewable and Sustainable Energy Reviews 109 (2019) 85–101. https://doi.org/10.1016/j.rser.2019.04.021.
[94] G. Lin, H. Kramer, J. Granderson, Building fault detection and diagnostics: Achieved savings, and methods to evaluate algorithm performance, Build Environ 168 (2020). https://doi.org/10.1016/j.buildenv.2019.106505.
[95] M. Ahern, D.T.J. O’Sullivan, K. Bruton, Implementation of the IDAIC framework on an air handling unit to transition to proactive maintenance, Energy Build 284 (2023) 112872. https://doi.org/10.1016/j.enbuild.2023.112872.
[96] C. Fan, F. Xiao, C. Yan, C. Liu, Z. Li, J. Wang, A novel methodology to explain and evaluate data-driven building energy performance models based on interpretable machine learning, Appl Energy 235 (2019) 1551–1560. https://doi.org/10.1016/j.apenergy.2018.11.081.
[97] J. Chen, L. Zhang, Y. Li, Y. Shi, X. Gao, Y. Hu, A review of computing-based automated fault detection and diagnosis of heating, ventilation and air conditioning systems, Renewable and Sustainable Energy Reviews 161 (2022). https://doi.org/10.1016/j.rser.2022.112395.
[98] M. Babadi Soultanzadeh, M.M. Ouf, M. Nik-Bakht, P. Paquette, S. Lupien, Fault detection and diagnosis in light commercial buildings’ HVAC systems: A comprehensive framework, application, and performance evaluation, Energy Build 316 (2024) 114341. https://doi.org/10.1016/j.enbuild.2024.114341.
[99] M. Babadi Soutanzadeh, M. Ouf, N.-B. Nik-Bakht, P. Paquette, S. Lupien, A Framework for Automated Fault Detection in Light Commercial Buildings HVAC System, in: ASHRAE Transactions 130, 2024: pp. 590–599. https://www.scopus.com/record/display.uri?eid=2-s2.0-85198914362&origin=resultslist (accessed July 28, 2024).
[100] J.Edward. Jackson, A user’s guide to principal components, Wiley, 1991.
[101] M. Ester, H.-P. Krigel, Sander, Xiaowei. Jorg. Xu, A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise, in: (KDD-96, 1996: pp. 226–231.
[102] A. Capozzoli, F. Lauro, I. Khan, Fault detection analysis using data mining techniques for a cluster of smart office buildings, Expert Syst Appl 42 (2015) 4324–4338. https://doi.org/10.1016/j.eswa.2015.01.010.
[103] G. Li, Y. Hu, H. Chen, J. Wang, Y. Guo, J. Liu, J. Li, Identification and isolation of outdoor fouling faults using only built-in sensors in variable refrigerant flow system: A data mining approach, Energy Build 146 (2017) 257–270. https://doi.org/10.1016/j.enbuild.2017.04.041.
[104] W.Y. Lee, J.M. House, N.H. Kyong, Subsystem level fault diagnosis of a building’s air-handling unit using general regression neural networks, Appl Energy 77 (2004) 153–170. https://doi.org/10.1016/S0306-2619(03)00107-7.
[105] Canadian Historical Climate Data, (n.d.). https://climate.weather.gc.ca/ (accessed August 31, 2023).
[106] P.J. Rousseeuw, Silhouettes: a graphical aid to the interpretation and validation of cluster analysis, 1987.
[107] K. Yan, L. Ma, Y. Dai, W. Shen, Z. Ji, D. Xie, Cost-sensitive and sequential feature selection for chiller fault detection and diagnosis, International Journal of Refrigeration 86 (2018) 401–409. https://doi.org/10.1016/j.ijrefrig.2017.11.003.
[108] M.A.F. Abdollah, R. Scoccia, M. Aprile, Transformer encoder based self-supervised learning for HVAC fault detection with unlabeled data, Build Environ 258 (2024). https://doi.org/10.1016/j.buildenv.2024.111568.
[109] John Bollinger, www.bollingerbands.com, (n.d.).
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