A model-based strategy for self-correction of sensor faults in variable air volume air handling units

References

• ASHRAE. 2009. ASHRAE Handbook: Fundamentals ( SI Ed.). Chapter 21: Duct design. Fluid resistance. Atlanta: ASHRAE.
   Google Scholar
• ASHRAE 2012. ASHRAE Handbook: Heating, Ventilating, and Air-Conditioning Systems and Equipment ( SI Ed.). Chapter 21: Fans. Temperature rise across fans. Atlanta: ASHRAE.
   Google Scholar
• Brambley, M.R., N. Fernandez, W. Wang, K.A. Cort, H. Cho, H. Ngo, and J.K. Goddard. 2011. Self-correcting controls for VAV system faults: Filter/fan/coil and VAV box sections. Final Project Report PNNL-20452, Pacific Northwest National Laboratory, Richland, WA.
   Google Scholar
• Castro, N., and H. Vaezi-Nejad. 2005. CITE-AHU, an automated commissioning tool for air-handling units. Proceedings of the 13th National Conference on Building Commissioning, New York City, NY, May 4–6.
   Google Scholar
• Chen, Y., and L. Lan. 2010. Fault detection, diagnosis and data recovery for a real building heating/cooling billing system. Energy Conversion and Management 51(5):1015–24.
   Web of Science ®Google Scholar
• Chillar, R.J., and R.J. Liesen. 2004. Improvement of the ASHRAE secondary toolkit SIMPLE cooling coil model for simulation. SimBuild 2004, IBPSA-USA National Conference, Boulder, CO, August 4–6.
   Google Scholar
• Choinière, D. 2008. DABOTM: A BEMS assisted on-going commissioning tool. 16th National Conference on Building Commissioning, Newport Beach, CA, April 22–24.
   Google Scholar
• Choinière, D., and M. Corsi. 2003. A BEMS-assisted commissioning tool to improve the energy performance of hvac systems. Proceedings of the Third International Conference for Enhanced Building Operations, Berkeley, CA, October 13–15.
   Google Scholar
• Clark, D.R. 1985. HVACSIM+buildings system and equipment simulation program reference manual. Gaithersburg, MD: US National Institute for Standards and Technology.
   Google Scholar
• Dep, K. 2001. Multi-Objective Optimization Using Evolutionary Algorithms. Chapter 4: Evolutionary Algorithms, pp. 81–165. New York: John Wiley & Sons.
   Google Scholar
• Dexter, A.L., and J. Pakanen. 2001. Annex 34: Computer aided evaluation of HVAC system performance. Final Report, International Energy Agency, Oxford, United Kingdom.
   Google Scholar
• Diaz-Gomez, P., and D. Hougen. 2007. Initial population for genetic algorithms: A metric approach. Proceedings of the 2007 International Conference on Genetic and Evolutionary Methods (GEM 2007). Las Vegas, NV, June 25–28.
   Google Scholar
• Fernandez, N., M. Brambley, and S. Katipamula. 2009a. Self-correcting HVAC controls: Algorithms for sensors and dampers in air-handling units. PNNL-19104, Pacific Northwest National Laboratory, Richland, WA.
   Google Scholar
• Fernandez, N., M. Brambley, S. Katipamula, H. Cho, J. Goddard, and L. Dinh. 2009b. Self-correcting HVAC controls. Project Final Report PNNL- 19074, Pacific Northwest National Laboratory, Richland, WA.
   Google Scholar
• Goldberg, D.E. 1989. Genetic Algorithms in Search, Optimization and Machine Learning. Chapter 3: Computer implementation of a genetic algorithm, pp. 59–86. Reading, MA: Addison-Wesley.
   Google Scholar
• Gotshall, S., and B. Rylander. 2002. Optimal population size and the genetic algorithm. Population 100(400):900.
   Google Scholar
• Holmes, M.J. 1982. The simulation of heating and cooling coils for performance analysis. Proceedings of the 1st International Conference on System Simulation in Buildings, Liége, Belgium, pp. 245–28.
   Google Scholar
• Huang, X.H. 2004. Sensor fault diagnosis and reconstruction of engine control system based on autoassociative neural network. Chinese Journal of Aeronautics 17(1):23–7.
   Google Scholar
• Hyvarinen, J., and S. Karki. 1996. IEA Annex 25: Building optimization and fault diagnosis source book. Final Report, International Energy Agency, Espoo, Finland.
   Google Scholar
• Isermann, R. 2005. Model-based fault-detection and diagnosis—status and applications. Annual Reviews in Control 29(1):71–85.
   Web of Science ®Google Scholar
• Isermann, R., and P. Ballé. 1997. Trends in the application of model-based fault detection and diagnosis of technical processes. Control Engineering Practice 5(5):709–19.
   Web of Science ®Google Scholar
• Li, Z., C.J. Paredis, G. Augenbroe, and G. Huang. 2012. A rule augmented statistical method for air-conditioning system fault detection and diagnostics. Energy and Buildings 54:154–9.
   Web of Science ®Google Scholar
• Nassif, N., S. Moujaes, and M. Zaheeruddin. 2008. Self-tuning dynamic models of HVAC system components. Energy and Buildings 40(9):1709–20.
   Web of Science ®Google Scholar
• Natural Resources Canada. 2009. Palais des congrès de montréal: A more intelligent and efficient building because of DABOTM. CanmetENERGY. http://www.nrcan.gc.ca/files/canmetenergy/files/pubs/DABO_Palais_des_congres_en.pdf
   Google Scholar
• Neumann, C., H. Yoshida, D. Choinière, and N. Milesi Ferretti. 2010. IEA Annex 47: Report 2. Commissioning Tools for Existing and Low Energy Buildings. International Energy Agency, Paris, France.
   Google Scholar
• Park, C. 2008. HVACSIM+ users guide update. US National Institute for Standards and Technology. Gaithersburg, MD.
   Google Scholar
• Steinberg, M. 2005. Historical overview of research in reconfigurable flight control. Proceedings of the Institution of Mechanical Engineers, Part G:Journal of Aerospace Engineering 219(4):263–75.
   Web of Science ®Google Scholar
• Taylor, S. 2007. Increasing efficiency with VAV system static pressure set-point reset. ASHRAE Journal June 24–32.
   Google Scholar
• Tomayko, J.E. 2003. The Story of Self-Repairing Flight Control Systems. C. Gelzer, ed. Section 2: The Self-Repairing Flight Control System, pp. 12–28. Edwards, CA: NASA Dryden Flight Research Center.
   Google Scholar
• Tsoy, Y. 2003. The influence of population size and search time limit on genetic algorithm. Proceedings of the 7th Korea-Russia International Symposium on Science and Technology (KORUS 2003) 3:181–7.
   Google Scholar
• Visier, J.C. 2004. IEA Annex 40: Commissioning of building HVAC systems for improving energy performance. Final Report—commissioning tools for improved energy performance. International Energy Agency—ECBCS.
   Google Scholar
• Wang, H., Y. Chen, W.H. Chan, and J. Qin. 2012a. An online fault diagnosis tool of VAV terminals for building management and control systems. Automation in Construction 22:203–11.
   Web of Science ®Google Scholar
• Wang, H., Y. Chen, W.H. Chan, J. Qin, J. Wang. 2012b. Online model-based fault detection and diagnosis strategy for VAV air handling units. Energy and Buildings 55:252–63.
   Web of Science ®Google Scholar
• Wang, S., and X. Jin. 2000. Model-based optimal control of VAV air-conditioning system using genetic algorithm. Building and Environment 35(6):471–87.
   Web of Science ®Google Scholar
• Wang, S.W., and J.Y. Qin. 2005. Sensor fault detection and validation of VAV terminals in air conditioning systems. Energy Conversion and Management 46(15–16):2482–500.
   Web of Science ®Google Scholar
• Wang, S.W., and F. Xiao. 2006. Sensor fault detection and diagnosis of air-handling units using a condition-based adaptive statistical method. Science and Technology for the Built Environment 12(1):127–50.
   Google Scholar
• Xiao, F., S.W. Wang, X.H. Xu, and G. Ge. 2009. An isolation enhanced PCA method with expert-based multivariate decoupling for sensor FDD in air-conditioning systems. Applied Thermal Engineering 29(4):712–22.
   Web of Science ®Google Scholar
• Xiao, F., Y. Zhao, J. Wen, and S.W. Wang. 2014. Bayesian network based FDD strategy for variable air volume terminals. Automation in Construction 41:106–18.
   Web of Science ®Google Scholar
• Xu, P., P. Haves, and D. Curtil. 2006. A library of HVAC component models for use in automated diagnostics. SimBuild 2006, IBPSA-USA Second National Conference, Cambridge, MA, August 2–4.
   Google Scholar