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Open Access

Semi-Supervised Machine Learning for Fault Detection and Diagnosis of a Rooftop Unit

Department of Mechanical Engineering, School of Engineering, University of Connecticut, Storrs, CT 06269, USA
Department of Computer Science and Engineering, University of Connecticut, Storrs, CT 06269, USA
Department of Mechanical and Industrial Engineering, Tagliatela College of Engineering, University of New Haven, West Haven, CT 06516, USA

† Mohammed G. Albayati and Jalal Faraj contributed equally to this article.

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Abstract

Most heating, ventilation, and air-conditioning (HVAC) systems operate with one or more faults that result in increased energy consumption and that could lead to system failure over time. Today, most building owners are performing reactive maintenance only and may be less concerned or less able to assess the health of the system until catastrophic failure occurs. This is mainly because the building owners do not previously have good tools to detect and diagnose these faults, determine their impact, and act on findings. Commercially available fault detection and diagnostics (FDD) tools have been developed to address this issue and have the potential to reduce equipment downtime, energy costs, maintenance costs, and improve occupant comfort and system reliability. However, many of these tools require an in-depth knowledge of system behavior and thermodynamic principles to interpret the results. In this paper, supervised and semi-supervised machine learning (ML) approaches are applied to datasets collected from an operating system in the field to develop new FDD methods and to help building owners see the value proposition of performing proactive maintenance. The study data was collected from one packaged rooftop unit (RTU) HVAC system running under normal operating conditions at an industrial facility in Connecticut. This paper compares three different approaches for fault classification for a real-time operating RTU using semi-supervised learning, achieving accuracies as high as 95.7% using few-shot learning.

Keywords

energy efficiencyfault detection and diagnosticsdata-driven modelingsemi-supervised machine learningfault classificationheating, ventilation, and air-conditioning

References

[1]
What’s on Your Roof? Rooftop Unit (RTU) Efficiency Advice and Guidance from the Advanced RTU Campaign, https://www.energy.gov/eere/buildings/articles/what-s-your-roof-rooftop-unit-rtu-efficiency-advice-and-guidance-advanced, 2021.
[2]
A. Ebrahimifakhar, A. Kabirikopaei, and D. Yuill, Data-driven fault detection and diagnosis for packaged rooftop units using statistical machine learning classification methods, Energy Build., vol. 225, p. 110318, 2020.
Crossref Google Scholar
[3]
J. Braun and D. Yuill, FDD Evaluator 0.1, Ray W. Herrick Laboratories-Purdue University, West Lafayette, 2013.
Crossref
[4]
S. Deshmukh, S. Samouhos, L. Glicksman, and L. Norford, Fault detection in commercial building VAV AHU: A case study of an academic building, Energy Build., vol. 201, pp. 163–173, 2019.
Crossref Google Scholar
[5]
J. Granderson, R. Singla, E. Mayhorn, P. Ehrlich, D. Vrabie, and S. Frank, Characterization and Survey of Automated Fault Detection and Diagnostics Tools. Berkeley, CA, USA: Lawrence Berkeley National Laboratory, 2017.
[6]
J. Wall and Y. Guo, Evaluation of Next-Generation Automated Fault Detection & Diagnostics (FDD) Tools for Commercial Building Energy Efficiency. CSIRO Energy for Low Carbon Living CRC, 2018.
[7]
J. Granderson, G. Lin, R. Singla, E. Mayhorn, P. Ehrlich, D. Vrabie, and S. Frank, Commercial Fault Detection and Diagnostics Tools: What They Offer, How They Differ, and What’s Still Needed. Berkeley, CA, USA: Lawrence Berkeley National Laboratory, 2018.
[8]
W. Kim and S. Katipamula, A review of fault detection and diagnostics methods for building systems, Sci. Technol. Built Environ., vol. 24, no. 1, pp. 3–21, 2018.
Crossref Google Scholar
[9]
C. Robinson, B. Dilkina, J. Hubbs, W. Zhang, S. Guhathakurta, M. A. Brown, and R. M. Pendyala, Machine learning approaches for estimating commercial building energy consumption, Appl. Energy, vol. 208, pp. 889–904, 2017.
Crossref Google Scholar
[10]
K. Yan, J. Huang, W. Shen, and Z. Ji, Unsupervised learning for fault detection and diagnosis of air handling units, Energy Build., vol. 210, p. 109689, 2020.
Crossref Google Scholar
[11]
K. Yan, C. W. Zhong, Z. Ji, and J. Huang, Semi-supervised learning for early detection and diagnosis of various air handling unit faults, Energy Build., vol. 181, pp. 75–83, 2018.
Crossref Google Scholar
[12]
N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, SMOTE: Synthetic minority over-sampling technique, J. Artif. Intell. Res., vol. 16, no. 1, pp. 321–357, 2002.
Crossref Google Scholar
[13]
K. Yan, A. Chong, and Y. Mo, Generative adversarial network for fault detection diagnosis of chillers, Build. Environ., vol. 172, p. 106698, 2020.
Crossref Google Scholar
[14]
K. Yan, Chiller fault detection and diagnosis with anomaly detective generative adversarial network, Build. Environ., vol. 201, p. 107982, 2021.
Crossref Google Scholar
[15]
A. Ebrahimifakhar, D. Yuill, and A. Kabirikopaei, Application of machine learning classification methods in fault detection and diagnosis of rooftop units, in Proc. 18t⁢h Int. Refrigeration and Air Conditioning Conf., West Lafayette, IN, USA, 2021, p. 2137.
Google Scholar
[16]
F. Cheng, W. Cai, X. Zhang, H. Liao, C. Cui, Fault detection and diagnosis for air handling unit based on multiscale convolutional neural networks, Energy Build., vol. 236, p. 110795, 2021.
Crossref Google Scholar
[17]
K. P. Lee, B. H. Wu, and S. L. Peng, Deep-learning-based fault detection and diagnosis of air-handling units, Build. Environ., vol. 157, pp. 24–33, 2019.
Crossref Google Scholar
[18]
H. Wang, D. Feng, and K. Liu, Fault detection and diagnosis for multiple faults of VAV terminals using self-adaptive model and layered random forest, Build. Environ., vol. 193, p. 107667, 2021.
Crossref Google Scholar
[19]
R. Chintala, J. Winkler, and X. Jin, Automated fault detection of residential air-conditioning systems using thermostat drive cycles, Energy Build., vol. 236, p. 110691, 2021.
Crossref Google Scholar
[20]
I. Cohen, F. G. Cozman, N. Sebe, M. C. Cirelo, and T. S. Huang, Semisupervised learning of classifiers: Theory, algorithms, and their application to human-computer interaction, IEEE Trans. Pattern Anal. Mach. Intell., vol. 26, no. 12, pp. 1553–1566, 2004.
Crossref Google Scholar
[21]
M. S. Mirnaghi and F. Haghighat, Fault detection and diagnosis of large-scale HVAC systems in buildings using data-driven methods: A comprehensive review, Energy Build., 229, p. 110492, 2020.
Crossref Google Scholar
[22]
C. Fan, X. Liu, P. Xue, and J. Wang, Statistical characterization of semi-supervised neural networks for fault detection and diagnosis of air handling units, Energy Build., vol. 234, p. 110733, 2021.
Crossref Google Scholar
[23]
B. Li, F. Cheng, X. Zhang, C. Cui, and W. Cai, A novel semi-supervised data-driven method for chiller fault diagnosis with unlabeled data, Appl. Energy., vol. 285, p. 116459, 2021.
Crossref Google Scholar
[24]
M. Albayati, R. Gorthala, A. Thompson, P. Patil, and A. Hacker, Bringing automated fault detection and diagnostics tools for HVAC&R into the mainstream, J. Eng. Sustain. Build. Cities, vol. 1, no. 3, p. 030902, 2020.
Crossref Google Scholar
[25]
W. Kim and J. E. Braun, Performance evaluation of a virtual refrigerant charge sensor, Int. J. Refrig., vol. 36, no. 3, pp. 1130–1141, 2013.
Crossref Google Scholar
[26]
W. Kim and J. E. Braun, Extension of a virtual refrigerant charge sensor, Int. J. Refrig., vol. 55, pp. 224–235, 2015.
Crossref Google Scholar
[27]
H. Li and J. E. Braun, Development, evaluation, and demonstration of a virtual refrigerant charge sensor, HVAC&R Res., vol. 15, no. 1, pp. 117–136, 2009.
Crossref Google Scholar
[28]
M. Mehrabi and D. Yuill, Generalized effects of faults on normalized performance variables of air conditioners and heat pumps, Int. J. Refrig., vol. 85, pp. 409–430, 2018.
Crossref Google Scholar
[29]
M. Mehrabi and D. Yuill, Generalized effects of refrigerant charge on normalized performance variables of air conditioners and heat pumps, Int. J. Refrig., vol. 76, pp. 367–384, 2017.
Crossref Google Scholar
[30]
D. P. Yuill and J. E. Braun, Evaluating the performance of fault detection and diagnostics protocols applied to air-cooled unitary air-conditioning equipment, HVAC&R Res., vol. 19, no. 7, pp. 882–891, 2013.
Crossref Google Scholar
[31]
A. Thompson, R. Gorthala, M. Albayati, and P. Pati, RTU-HVAC real-time operating data from unit in field, https://data.mendeley.com/datasets/9h6gpbhj5k/1, 2021.
[32]
L. M. R. Baccarini, V. V. R. E Silva, B. R. de Menezes, and W. M. Caminhas, SVM practical industrial application for mechanical faults diagnostic, Expert Syst. Appl., vol. 38, no. 6, pp. 6980–6984, 2011.
Crossref Google Scholar
[33]
V. N. Vapnik, The Nature of Statistical Learning Theory, New York, NY, USA: Springer, 2000.
Crossref
[34]
P. Arora, , and S. Varshney, Analysis of K-means and K-medoids algorithm for big data, Proced. Comput. Sci., vol. 78, pp. 507–512, 2016.
Crossref Google Scholar
Big Data Mining and Analytics
Volume 6 Issue 2,
June 2023
Pages 170-184
DOI: 10.26599/BDMA.2022.9020015
Cite this article:
Albayati MG, Faraj J, Thompson A, et al. Semi-Supervised Machine Learning for Fault Detection and Diagnosis of a Rooftop Unit. Big Data Mining and Analytics, 2023, 6(2): 170-184. https://doi.org/10.26599/BDMA.2022.9020015

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Received: 16 June 2022
Accepted: 30 June 2022
Published: 26 January 2023
© The author(s) 2023.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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