A Bridge Approach to Fault Diagnosis in Buildings
Main Article Content
Abstract
Nowadays, building's power consumption represents the most important portion of the global consumption (about 40-45%). To use more efficient energy sources, it requires not only the improvement in materials as well as new technology measures using less energy, but requires also the detection of faults that can occur during building life. These faults cause not only serious energy losses, but also human discomfort in the buildings. Thanks to sensor network, discomfort or failure alarms can be detected, which identify some issues in buildings. An alarm must be analysis to identify the faults and fix them as quickly as possible in order to maintain building performance. The aim of this paper is to study for application of diagnostic theories in the building. A Bridge approach is used as a diagnosis tools in the buildings. An application to a smart building is implemented to face this fault diagnosis in buildings problem.
Keywords
Energy Smart-Home, Diagnosis, Fault detection, Bridge approach
Article Details
References
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[13] Katipamula, S., M. R. Brambley. 2005. Methods for fault detection, diagnostics, and prognostics for building systems a review part 1 & 2, HVAC&R Research, vol. 11.
[14] Cordier, M. O., P. Dague, F. Levy, J. Montmain, M. Staroswiecki, L. Trave-Massuyes. 2004. Conflicts versus analytical redundancy relations: a comparative analysis of the model based diagnosis approach from the artificial intelligence and automatic control perspectives in Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions 34(5):2163-21.
[2] Song, L., Liu, M., Claridge, D. E., & Haves, P. (2003). Study of on-line simulation for whole building level energy consumption fault detection and optimization. In Proceedings of architectural engineering, building integration solutions (pp.76-83)
[3] Wu, S., & Sun, J. Q. (2011). Cross-level fault detection and diagnosis of building HVAC systems. Building and Environment, 46(8), 1558-1566.
[4] Hyvarinen, J., and K. Satu, 1996. Annex-25, Building Optimization and Fault Diagnosis Source Book. International Energy Agency Building, Automated Fault Detection and Diagnosis Methods in Real Buildings. Technical Research Centre of Finland, Laboratory of Heating and Ventilation, Finland
[5] Dexter, A., and J. Pakanen. 2001. Annex-34, International Energy Agency Building, Demonstrating Automated Fault Detection and Diagnosis Methods in Real Buildings. Technical Research Centre of Finland, Laboratory of Heating and Ventilation, Espoo, Finland.
[6] Bynum, J. D., D. E. Claridge, J. M. Curtin. 2012. Development and testing of an automated building commissioning analysis tool (ABCAT), Journal of Energy and Building, 55:607-617.
[7] Katipamula, S., M. R. Brambley, N. N. Bauman, R. G. Pratt. 2003. Enhancing building operation through automated diagnostics: Field test result. In proceeding of the third international conference for Enhanced Building Operation (ICEBO), California USA.
[8] Bonvini, M., M. A. Piette, M. Wetter, J. Granderson, and M. D. Sohn. 2014. Bridging the gap between simulation and real world an application to FDD, ACEEE Summer Study on Energy Efficiency in Buildings California, USA.
[9] Katipamula, S., R. G. Pratt, D. P. Chassin, Z. T. Taylor, K. Gowri, and M. R. Brambley. 1999. Automated fault detection and diagnostics for outdoor air ventilation systems and economizers: Methodology and results from field testing. ASHRAE Transactions 105(1).
[10] House, J. M., H. Vaezi-Nejad, and J. M. Whitcomb. 2001. An expert rule set for fault detection in air-handling units. ASHRAE Transactions 107(1).
[11] Glass, A. S., P. Gruber, M. Roos, and J. Todtli. 1995. Qualitative model-based fault detection in air-handling units. IEEE Control Systems Magazine 15(4):11-22.
[12] Haris D. Konstantinos, D. Patlitzianas, Κ. Iatropoulous, J. Psarras. 2006. Intelligent building energy management system using rule set, Journal of Science Direct 42:3562-3569.
[13] Katipamula, S., M. R. Brambley. 2005. Methods for fault detection, diagnostics, and prognostics for building systems a review part 1 & 2, HVAC&R Research, vol. 11.
[14] Cordier, M. O., P. Dague, F. Levy, J. Montmain, M. Staroswiecki, L. Trave-Massuyes. 2004. Conflicts versus analytical redundancy relations: a comparative analysis of the model based diagnosis approach from the artificial intelligence and automatic control perspectives in Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions 34(5):2163-21.