A bi-level data-driven framework for fault-detection and diagnosis of HVAC systems

Movahed, Paria; Taheri, Saman; Razban, Ali

doi:10.1016/j.apenergy.2023.120948

A bi-level data-driven framework for fault-detection and diagnosis of HVAC systems

Journal Article · Fri Mar 17 00:00:00 EDT 2023 · Applied Energy

DOI:https://doi.org/10.1016/j.apenergy.2023.120948· OSTI ID:2417656

Movahed, Paria ^[1]; ^[2]; Razban, Ali ^[2]

Indiana Univ.-Purdue Univ. Indianapolis (IUPUI), Indianapolis, IN (United States); OSTI
Indiana Univ.-Purdue Univ. Indianapolis (IUPUI), Indianapolis, IN (United States)

Long-term operation of heating, ventilation, and air conditioning (HVAC) systems will eventually lead to a range of HVAC system failures, resulting in excessive energy consumption and maintenance costs. Here, to avoid HVAC malfunctioning, fault detection diagnostic (FDD) is utilized as a common practice. Machine learning methods have lately received considerable interest for FDD analysis of HVAC systems due to their high detection accuracy. Meanwhile, HVAC malfunctions are regarded as rare occurrences, hence normal operating data samples are much more accessible than data samples in faulty and malfunctioning conditions. The dominating frequency of normal operation in HVAC datasets has also led to heavily biased classification algorithms within the literature. Moreover, the focus of previous literature has been on increasing the accuracy of the models which leads to a high number of false positives (misleading alarms) in the system. In order to enhance the performance of diagnostic procedures and fill the mentioned gaps, this study proposes a novel data-driven framework. A bi-level machine learning framework is developed for diagnosing faults in air handling units (AHUs) and rooftop units (RTUs) based on principal component analysis (PCA), time series anomaly detection, and random forest (RF). It is shown that PCA can reduce the dataset dimension with one principal component accounting for 95% of data variance. Also, the random forest could classify the faults with 89% precision for single-zone AHU, 85% precision for RTU, and 79% for multi-zone AHU. By proposing this framework, three persistent challenges are addressed: (I) minimizing false positives; (II) accounting for data imbalance; and (III) normal condition monitoring of equipment.

View Accepted Manuscript (DOE)

Research Organization:: Indiana Univ., Bloomington, IN (United States)

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

Grant/Contract Number:: EE0007711

OSTI ID:: 2417656

Alternate ID(s):: OSTI ID: 1963127

Journal Information:: Applied Energy, Journal Name: Applied Energy Journal Issue: C Vol. 339; ISSN 0306-2619

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

References (43)

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
Automation-assisted fault detection of an air-handling unit; implementing the method in a real building Pakanen, Jouko E.; Sundquist, Tero Energy and Buildings, Vol. 35, Issue 2 https://doi.org/10.1016/S0378-7788(02)00050-6	journal	February 2003
Carbon reduction in commercial building operations: A provincial retrospection in China Li, Kai; Ma, Minda; Xiang, Xiwang Applied Energy, Vol. 306 https://doi.org/10.1016/j.apenergy.2021.118098	journal	January 2022
Historical decarbonization of global commercial building operations in the 21st century Xiang, Xiwang; Ma, Minda; Ma, Xin Applied Energy, Vol. 322 https://doi.org/10.1016/j.apenergy.2022.119401	journal	September 2022
Distributed scheduling of HVACs based on transactive energy and ADMM Song, Meng; Gao, Ciwei; Ma, Sisi Applied Energy, Vol. 325 https://doi.org/10.1016/j.apenergy.2022.119831	journal	November 2022
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
Performance and combustion characteristics of a diesel engine fuelled by butanol–biodiesel–diesel blends Ibrahim, Amr Applied Thermal Engineering, Vol. 103 https://doi.org/10.1016/j.applthermaleng.2016.04.144	journal	June 2016
An effective fault diagnosis method for centrifugal chillers using associative classification Huang, Ronggeng; Liu, Jiangyan; Chen, Huanxin Applied Thermal Engineering, Vol. 136 https://doi.org/10.1016/j.applthermaleng.2018.03.041	journal	May 2018
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
Operational carbon transition in the megalopolises’ commercial buildings Ma, Minda; Feng, Wei; Huo, Jingwen Building and Environment, Vol. 226 https://doi.org/10.1016/j.buildenv.2022.109705	journal	December 2022
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
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
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
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
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
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
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
A semi-supervised approach to fault detection and diagnosis for building HVAC systems based on the modified generative adversarial network Li, Bingxu; Cheng, Fanyong; Cai, Hui Energy and Buildings, Vol. 246 https://doi.org/10.1016/j.enbuild.2021.111044	journal	September 2021
Fault detection diagnostic for HVAC systems via deep learning algorithms Taheri, Saman; Ahmadi, Amirhossein; Mohammadi-Ivatloo, Behnam Energy and Buildings, Vol. 250 https://doi.org/10.1016/j.enbuild.2021.111275	journal	November 2021
Techno-economic optimization of biogas-fueled micro gas turbine cogeneration systems in sewage treatment plant Movahed, Paria; Avami, Akram Energy Conversion and Management, Vol. 218 https://doi.org/10.1016/j.enconman.2020.112965	journal	August 2020
Privacy-preserving federated learning: Application to behind-the-meter solar photovoltaic generation forecasting Hosseini, Paniz; Taheri, Saman; Akhavan, Javid Energy Conversion and Management, Vol. 283 https://doi.org/10.1016/j.enconman.2023.116900	journal	May 2023
Automated load pattern learning and anomaly detection for enhancing energy management in smart buildings Capozzoli, Alfonso; Piscitelli, Marco Savino; Brandi, Silvio Energy, Vol. 157 https://doi.org/10.1016/j.energy.2018.05.127	journal	August 2018
Fault location determination in three-terminal transmission lines connected to industrial microgrids without requiring fault classification data and independent of line parameters Abasi, Mahyar; Rohani, Arash; Hatami, Farhad International Journal of Electrical Power & Energy Systems, Vol. 131 https://doi.org/10.1016/j.ijepes.2021.107044	journal	October 2021
An overall performance index for characterizing the economic impact of faults in direct expansion cooling equipment Li, Haorong; Braun, James E. International Journal of Refrigeration, Vol. 30, Issue 2 https://doi.org/10.1016/j.ijrefrig.2006.07.026	journal	March 2007
An enhanced chiller FDD strategy based on the combination of the LSSVR-DE model and EWMA control charts Tran, Dinh Anh Tuan; Chen, Youming; Ao, Hung Linh International Journal of Refrigeration, Vol. 72 https://doi.org/10.1016/j.ijrefrig.2016.07.024	journal	December 2016
A figure of merit for overall performance and value of AFDD tools Yuill, David P.; Braun, James E. International Journal of Refrigeration, Vol. 74 https://doi.org/10.1016/j.ijrefrig.2016.11.015	journal	February 2017
A novel ionic liquid/polyoxomolybdate based sensor for ultra-high sensitive monitoring of Al(III): Optimization by Taguchi statistical design Ramezani, Soleyman; Jahani, Rana; Mashhadizadeh, Mohammad Hossein Journal of Electroanalytical Chemistry, Vol. 814 https://doi.org/10.1016/j.jelechem.2018.02.041	journal	April 2018
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
Model predictive control of heating, ventilation, and air conditioning (HVAC) systems: A state-of-the-art review Taheri, Saman; Hosseini, Paniz; Razban, Ali Journal of Building Engineering, Vol. 60 https://doi.org/10.1016/j.jobe.2022.105067	journal	November 2022
A new strategy for low temperature gas sensing by nano-sized metal oxides: Development a new nerve agent simulant sensor Alizadeh, Taher; Jahani, Rana Materials Chemistry and Physics, Vol. 168 https://doi.org/10.1016/j.matchemphys.2015.11.019	journal	November 2015
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
Random Forests Breiman, Leo Machine Learning, Vol. 45, Issue 1, p. 5-32 https://doi.org/10.1023/A:1010933404324	journal	January 2001
Building fault detection data to aid diagnostic algorithm creation and performance testing Granderson, Jessica; Lin, Guanjing; Harding, Ari Scientific Data, Vol. 7, Issue 1 https://doi.org/10.1038/s41597-020-0398-6	journal	February 2020
Bi‐level power management strategy in harmonic‐polluted active distribution network including virtual power plants Rohani, Arash; Abasi, Mahyar; Beigzadeh, Arsalan IET Renewable Power Generation, Vol. 15, Issue 2 https://doi.org/10.1049/rpg2.12044	journal	January 2021
A robust pattern recognition-based fault detection and diagnosis (FDD) method for chillers Zhao, Yang; Xiao, Fu; Wen, Jin HVAC&R Research, Vol. 20, Issue 7 https://doi.org/10.1080/10789669.2014.938006	journal	October 2014
A review of fault detection and diagnostics methods for building systems Kim, Woohyun; Katipamula, Srinivas Science and Technology for the Built Environment, Vol. 24, Issue 1 https://doi.org/10.1080/23744731.2017.1318008	journal	April 2017
Performance Evaluation of Techniques for Identifying Abnormal Energy Consumption in Buildings Gaur, Megha; Makonin, Stephen; Bajic, Ivan V. IEEE Access, Vol. 7 https://doi.org/10.1109/ACCESS.2019.2915641	journal	January 2019
Identifying Unseen Faults for Smart Buildings by Incorporating Expert Knowledge With Data Li, Dan; Zhou, Yuxun; Hu, Guoqiang IEEE Transactions on Automation Science and Engineering, Vol. 16, Issue 3 https://doi.org/10.1109/TASE.2018.2876611	journal	July 2019
Semantic Diagnosis Approach for Buildings Ploennigs, Joern; Maghella, Michael; Schumann, Anika IEEE Transactions on Industrial Informatics, Vol. 13, Issue 6 https://doi.org/10.1109/TII.2017.2726001	journal	December 2017
CT-ML: Diagnosis of Breast Cancer Based on Ultrasound Images and Time-Dependent Feature Extraction Methods Using Contourlet Transformation and Machine Learning Hajipour Khire Masjidi, Behnam; Bahmani, Soufia; Sharifi, Fatemeh Computational Intelligence and Neuroscience, Vol. 2022 https://doi.org/10.1155/2022/1493847	journal	May 2022
Hybrid AC/DC Provisional Microgrid Planning Model Considering Converter Aging Rezaei, Omid; Mirzapour, Omid; Panahazari, Mohammad Electricity, Vol. 3, Issue 2 https://doi.org/10.3390/electricity3020014	journal	June 2022
Hybrid Random Forest and Support Vector Machine Modeling for HVAC Fault Detection and Diagnosis Tun, Wunna; Wong, Johnny Kwok-Wai; Ling, Sai-Ho Sensors, Vol. 21, Issue 24 https://doi.org/10.3390/s21248163	journal	December 2021

Similar Records

Modeling Air Handling Units to Create a Diverse Fault Dataset for FDD Innovation: Lessons Learned and Recommendations

Conference · Fri Oct 07 00:00:00 EDT 2022 · OSTI ID:1899588

Related Subjects

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION
42 ENGINEERING
Classification
Energy & Fuels
Engineering
Fault detection
HVAC
Machine learning

A bi-level data-driven framework for fault-detection and diagnosis of HVAC systems

Citation Formats

References (43)

Similar Records

Related Subjects