Improving the Prediction Accuracy of Data-Driven Fault Diagnosis for HVAC Systems by Applying the Synthetic Minority Oversampling Technique
Publication: Computing in Civil Engineering 2021
ABSTRACT
Faulty operation of heating, ventilation, and air conditioning (HVAC) systems in a building can lead to thermal discomfort, wasted energy, and shorter equipment life. Early fault diagnosis in HVAC systems is critical to maintaining indoor environmental comfort, saving energy, and preventing further deterioration of the system. Data-driven predictive models have emerged as a popular approach to fault diagnosis of HVAC systems. For an HVAC system, failures are a small probability event. Therefore, the prediction accuracy of these data-driven approaches is affected by imbalanced data sets. The normal data far outweigh the fault data. Predictive models trained with these imbalanced data sets will perform poorly in fault diagnosis of an HVAC system. To address this limitation, this study aims to examine the potential of the synthetic minority oversampling technique (SMOTE) for sampling fault data points to improve the accuracy of the fault predictive model. To that end, six months of operational data (for example, humidity and temperature) were collected from embedded sensors in the HVAC system. A SMOTE algorithm was applied to increase the ratio of failure to normal data from 0.02 to 0.3. Both the original and improved data sets were used to train the fault predictive model based on different supervised learning algorithms. Results indicated that data sets improved through the SMOTE algorithm increased the accuracy of the predictive model by about 20%. This improvement can lay the groundwork for increasingly proactive facility maintenance.
Get full access to this chapter
View all available purchase options and get full access to this chapter.
REFERENCES
Au-Yong, C. P., Ali, A. S., and Ahmad, F. (2014). Improving occupants’ satisfaction with effective maintenance management of HVAC system in office buildings. Automation in Construction, 43, 31–37. https://doi.org/10.1016/j.autcon.2014.03.013.
Beghi, A., Brignoli, R., Cecchinato, L., Menegazzo, G., Rampazzo, M., and Simmini, F. (2016). Data-driven Fault Detection and Diagnosis for HVAC water chillers. Control Engineering Practice, 53, 79–91. https://doi.org/10.1016/j.conengprac.2016.04.018.
Bouabdallaoui, Y., Lafhaj, Z., Yim, P., Ducoulombier, L., and Bennadji, B. (2021). Predictive Maintenance in Building Facilities: A Machine Learning-Based Approach. Sensors, 21(4), 1044. https://doi.org/10.3390/s21041044.
Budaiwi, I. M. (2007). An approach to investigate and remedy thermal-comfort problems in buildings. Building and Environment, 42(5), 2124–2131. https://doi.org/10.1016/j.buildenv.2006.03.010.
Chawla, N. V., Bowyer, K. W., Hall, L. O., and Kegelmeyer, W. P. (2002). SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16, 321–357. https://doi.org/10.1613/jair.953.
Chawla, N. V., Lazarevic, A., Hall, L. O., and Bowyer, K. W. (2003). SMOTEBoost: Improving prediction of the minority class in boosting. Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 2838, 107–119. https://doi.org/10.1007/978-3-540-39804-2_12.
Chen, W., Cheng, J. C. P., Chen, K., Fellow, P., Li, C. T., and Student, M. (2019). A BIM-based Location Aware AR Collaborative Framework for Facility Maintenance Management. Journal of Information Technology in Construction (ITcon), 24, 361. http://www.itcon.org/2019/19.
Cheng, J. C. P., Chen, W., Chen, K., and Wang, Q. (2020). Data-driven predictive maintenance planning framework for MEP components based on BIM and IoT using machine learning algorithms. Automation in Construction, 112, 103087. https://doi.org/10.1016/j.autcon.2020.103087.
Koch, C., Neges, M., König, M., and Abramovici, M. (2014). Natural markers for augmented reality-based indoor navigation and facility maintenance. Automation in Construction, 48, 18–30. https://doi.org/10.1016/j.autcon.2014.08.009.
Lavy, S., and Jawadekar, S. (2014). A Case Study of Using BIM and COBie for Facility Management. International Journal of Facility Management, 5(2).
Motamedi, A., Hammad, A., and Asen, Y. (2014). Knowledge-assisted BIM-based visual analytics for failure root cause detection in facilities management. Automation in Construction, 43, 73–83. https://doi.org/10.1016/j.autcon.2014.03.012.
Tobon-Mejia, D. A., Medjaher, K., Zerhouni, N., and Tripot, G. (2012). A data-driven failure prognostics method based on mixture of gaussians hidden markov models. IEEE Transactions on Reliability, 61(2), 491–503. https://doi.org/10.1109/TR.2012.2194177.
Xu, X., Chen, W., and Sun, Y. (2019). Over-sampling algorithm for imbalanced data classification. Journal of Systems Engineering and Electronics, 30(6), 1182–1191. https://doi.org/10.21629/JSEE.2019.06.12.
Information & Authors
Information
Published In
History
Published online: May 24, 2022
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.