A review of fault detection and diagnostics methods for building systems

References

• Alsaleem, F.M., R. Abiprojo, J. Arensmeier, and G. Hemmelgarn. 2014. HVAC system cloud based diagnostics model. International Refrigerant and Air Conditioning Conference, No 1508.
   Google Scholar
• Armstrong, P.R., C.R. Laughman, S.B. Leeb, and L.K. Norford. 2006. Detection of rooftop cooling unit faults based on electrical measurements. HVAC&R Research 12(1):151–75.
   Web of Science ®Google Scholar
• Arseniev, D.G., B.E. Lyubimov, and V.P. Shkodyrev. 2009. Intelligent fault detection and diagnostics system on rule-based neural network approach. In 2009 IEEE Control Applications & Intelligent Control, St. Petersburg Russia, July 8–10.
   Google Scholar
• Bashi, A., V.P. Jilkov, and X.R. Li. 2011. Fault detection for systems with multiple unknown modes and similar units and its application to HVAC. IEEE Transactions on Control Systems Technology 19(5):957–68.
   Web of Science ®Google Scholar
• Bonvini, M., M. Wetter, and M.D. Sohn. 2014a. An FMI-based toolchain for the adoption of model-base FDD. Proc. ASHRAE/IBPSA-USA Building Simulation Conference 2014, Atlanta, GA, September 10–12.
   Google Scholar
• Bonvini, M., M.D. Sohn, J. Granderson, M. Wetter, and M.A. Piette. 2014b. Robust on-line fault detection diagnosis for HVAC components based on nonlinear state estimation techniques. Applied Energy 124:156–66.
   Web of Science ®Google Scholar
• Brambley, M.R., N. Fernandez, W. Wang, K.A. Cort, H. Cho, H. Ngo, and J. Goddard. 2011. Self-correcting controls for VAV system faults, Filter/Fan/Coil and VAV Box. PNNL- 20452, U.S Department of Energy.
   Google Scholar
• Bruton, K., P. Raftery, P. O'Donovan, N. Aughney, M.M. Keane, and D.T.J. O'Sullivan. 2014. Development and alpha testing of a cloud based automated fault detection and diagnosis tool for air handling units. Automation in Construction 39:70–83.
   Web of Science ®Google Scholar
• Bynum, J.D., D.E. Claridge, and J.M. Curtin. 2012. Development and testing of an automated building commissioning analysis tool (ABCAT). Energy and Buildings 55:607–17.
   Web of Science ®Google Scholar
• Capozzoli, A., F. Lauro, and I. Khan. 2015. Fault detection analysis using data mining techniques for a cluster of smart office buildings. Expert Systems with Applications 42(9):4324–38.
   Web of Science ®Google Scholar
• CBECS. 2012. Commercial Buildings Energy Consumption Survey 2012, Energy Information Administration of U.S. Department of Energy, Washington, D.C. http://www.eia.gov/consumption/commercial/data/2012/#summary
   Google Scholar
• Cho, S.H., H.C. Yang, M. Zaheer-Uddin, and B.C. Ahn. 2005. Transient pattern analysis for fault detection and diagnosis of HVAC systems. Energy Conversion and Management 46(18):3103–16.
   Web of Science ®Google Scholar
• Choinière, D. 2008. DABO: A BEMS assisted on-going commissioning tool. National Conference on Building Commissioning, Newport Beach, CA, April 22–24.
   Google Scholar
• Cimini, G., A. Freddi, G. Ippoliti, A. Monteriù, and M. Pirro. 2015. A smart lighting system for visual comfort and energy savings in industrial and domestic use. Electric Power Components and Systems 43(15):1696–706.
   Web of Science ®Google Scholar
• Costa, A., M.M. Keane, J.I. Torrens, and E. Corry. 2013. Building operation and energy performance: Monitoring, analysis and optimisation toolkit. Applied Energy 101:310–6.
   Web of Science ®Google Scholar
• Cui, J., and S. Wang. 2005. A model-based online fault detection and diagnosis strategy for centrifugal chiller systems. International Journal of Thermal Sciences 44(10):986–99.
   Web of Science ®Google Scholar
• Dehestani, D., F. Eftekhari, Y. Guo, S. Ling, S. Su, and H. Nguyen. 2011. Online support vector machine application for model based fault detection and isolation of HVAC system. International Journal of Machine Learning and Computing 1(1):66.
   Google Scholar
• DOE. 2010. Energy Plus Energy Simulation Software, U.S. Department of Energy. http://apps1.eere.energy.gov/buildings/energyplus/
   Google Scholar
• Du, Z., X. Jin, and L. Wu. 2007. Fault detection and diagnosis based on improved PCA with JAA method in VAV systems. Building and Environment 42(9):3221–32.
   Web of Science ®Google Scholar
• Du, Z., X. Jin, and Y. Yang. 2009. Fault diagnosis for temperature, flow rate and pressure sensors in VAV systems using wavelet neural network. Applied Energy 86(9):1624–31.
   Web of Science ®Google Scholar
• Du, Z., B. Fan, X. Jin, and J. Chi. 2014. Fault detection and diagnosis for buildings and HVAC systems using combined neural networks and subtractive clustering analysis. Building and Environment 73:1–11.
   Web of Science ®Google Scholar
• Fan, B., Z. Du, X. Jin, X. Yang, and Y. Guo. 2010. A hybrid FDD strategy for local system of AHU based on artificial neural network and wavelet analysis. Building and Environment 45(12):2698–708.
   Web of Science ®Google Scholar
• Fernandez, N., M. Brambley, S. Katipamula, H. Cho, J. Goddard, and L. Dinh. 2009. Self-correcting HVAC controls: Project final report. PNNL-19074, U.S. Department of Energy.
   Google Scholar
• Fisera, R., and P. Stluka. 2012. Performance monitoring of the refrigeration system with minimum set of sensors. World Academy of Science, Engineering and Technology 6(7):396–401.
   Google Scholar
• Fontugne, R., J. Ortiz, N. Tremblay, P. Borgnat, P. Flandrin, K. Fukuda, D. Culler, and H. Esaki. 2013. Strip, bind, and search: A method for identifying abnormal energy consumption in buildings. In Proceedings of the 12th International Conference on Information Processing in Sensor Networks Philadelphia, PA, April 8–11, ACM; pp. 129–40.
   Google Scholar
• Freddi, A., G. Ippoliti, M. Marcantonio, D. Marchei, A. Monterius, and M. Pirro. 2013. A Fault Diagnosis and prognosis LED lighting system for increasing reliability in energy efficient buildings. In IET Conference on Control and Automation 2013: Uniting Problems and Solutions. London, UK, July 14, IET; pp. 1–6.
   Google Scholar
• Guo, Y., J. Wall, J. Li, and S. West. 2013. Intelligent model based fault detection and diagnosis for HVAC system using statistical machine learning methods. In ASHRAE 2013 Conference January Dallas, TX, January 26–30.
   Google Scholar
• Han, H., B. Gu, Y. Hong, and J. Kang. 2011a. Automated FDD of multiple-simultaneous faults (MSF) and the application to building chillers. Energy and Buildings 43(9):2524–32.
   Web of Science ®Google Scholar
• Han, H., B. Gu, T. Wang, and Z.R. Li. 2011b. Important sensors for chiller fault detection and diagnosis (FDD) from the perspective of feature selection and machine learning. International Journal of Refrigeration 34(2):586–99.
   Web of Science ®Google Scholar
• Hao, X., G. Zhang, and Y. Chen. 2005. Fault-tolerant control and data recovery in HVAC monitoring system. Energy and Buildings 37(2):175–80.
   Web of Science ®Google Scholar
• Haves, P., M. Kim, M. Najafi, and P. Xu. 2007. A semi-automated commissioning tool for VAV air handling units: Functional test analyzer. ASHRAE Transactions 113(LBNL–60979).
   Google Scholar
• He, D., C. Jiang, R.G. Harley, T.G. Habetler, and R. Ding. 2015. A new electrical method on airflow fault detection of air handling unit (AHU). In Power Systems Conference (PSC), 2015 Clemson University, Clemson, SC, March 10-13, IEEE; pp. 1–5.
   Google Scholar
• He, H., D. Menicucci, T. Caudell, and A. Mammoli. 2011. Real-time fault detection for solar hot water systems using adaptive resonance theory neural networks. In ASME 2011 5th International Conference on Energy Sustainability, Washington, DC, August 7–10, American Society of Mechanical Engineers; pp. 1059–65.
   Google Scholar
• He, H., T.P. Caudell, D.F. Menicucci, and A.A. Mammoli. 2012. Application of adaptive resonance theory neural networks to monitor solar hot water systems and detect existing or developing faults. Solar Energy 86(9):2318–33.
   Web of Science ®Google Scholar
• Hjortland, A. 2014. Probabilistic fault detection and diagnosis for packaged air-conditioner outdoor-air economizers. Master's Thesis, School of Mechanical Engineering, Purdue University, West Lafayette, IN.
   Google Scholar
• Hou, Z., Z. Lian, Y. Yao, and X. Yuan. 2006. Data mining based sensor fault diagnosis and validation for building air conditioning system. Energy Conversion and Management 47(15):2479–90.
   Web of Science ®Google Scholar
• 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):858–71.
   Google Scholar
• Jacob, D., S. Dietz, S. Komhard, C. Neumann, and S. Herkel. 2010. Black-box models for fault detection and performance monitoring of buildings. Journal of Building Performance Simulation 3(1):53–62.
   Web of Science ®Google Scholar
• Jin, X., H. Ren, and X. Xiao. 2005. Prediction-based online optimal control of outdoor air of multi-zone VAV air conditioning systems. Energy and Buildings 37(9):939–44.
   Web of Science ®Google Scholar
• Jin, X., and Z. Du. 2006. Fault tolerant control of outdoor air and AHU supply air temperature in VAV air conditioning systems using PCA method. Applied Thermal Engineering 26(11):1226–37.
   Web of Science ®Google Scholar
• Jones, C.B. 2015. Fault detection and diagnostics of an HVAC sub-system using adaptive resonance theory neural networks. Ph.D. Thesis, School of Civil Engineering of the University of New Mexico, Albuquerque, New Mexico.
   Google Scholar
• Joshi, P.C., T. Kuruganti, and C.E. Duty. 2015a. Printed and Hybrid Electronics Enabled by Digital Additive Manufacturing Technologies. Additive manufacturing: Innovations, advances, and applications. T.S. Srivatsan, and T.S. Sudarshan, eds. Abingdon, UK: Taylor & Francis; pp. 131–53.
   Google Scholar
• Joshi, P.C., T. Kuruganti, and S. Killough. 2015b. Impact of pulse thermal processing on the properties of inkjet printed metal and flexible sensors. ECS Journal of Solid State Science and Technology 4(4):3091–6. DOI: 10.1149/2.0161504jss
   Web of Science ®Google Scholar
• Joshi, P.C., M. Shao, K. Xiao, S.M. Killough, T. Kuruganti, and C.E. Duty. 2015c. Low temperature integration of metal oxide thin films for flexible electronic applications. American Vacuum Society 60th International Symposium & Exhibition, October 27–November 1, Long Beach, CA.
   Google Scholar
• Katipamula, S., and M.R. Brambley. 2005a. Review article: Methods for fault detection, diagnostics, and prognostics for building systems—A review, Part I. HVAC&R Research 11(1):3–25.
   Web of Science ®Google Scholar
• Katipamula, S., and M.R. Brambley. 2005b. Review article: Methods for fault detection, diagnostics, and prognostics for building systems—A review, part II. HVAC&R Research 11(2):169–87.
   Web of Science ®Google Scholar
• Keir, M.C., and A.G. Alleyne. 2006. Dynamic modeling, control, and fault detection in vapor compression systems. Air Conditioning and Refrigeration Center. College of Engineering. University of Illinois at Urbana-Champaign.
   Google Scholar
• Kim, M., S.H. Yoon, W.V. Payne, and P.A. Domanski. 2008. Cooling mode fault detection and diagnosis method for a residential heat pump. NIST Special Publication, 1087.
   Google Scholar
• Kim, W. 2013. Fault detection and diagnosis for air conditioners and heat pumps based on virtual sensors. Ph.D. Thesis, School of Mechanical Engineering, Purdue University, West Lafayette, IN.
   Google Scholar
• Kocyigit, N. 2015. Fault and sensor error diagnostic strategies for a vapor compression refrigeration system by using fuzzy inference systems and artificial neural network. International Journal of Refrigeration 50:69–79.
   Web of Science ®Google Scholar
• Lauro, F., F. Moretti, A. Capozzoli, I. Khan, S. Pizzuti, M. Macas, and S. Panzieri. 2014. Building fan coil electric consumption analysis with fuzzy approaches for fault detection and diagnosis. Energy Procedia 62:411–20.
   Google Scholar
• Lee, T.S., and W.C. Lu. 2010. An evaluation of empirically-based models for predicting energy performance of vapor-compression water chillers. Applied Energy 87(11):3486–93.
   Web of Science ®Google Scholar
• Lee, W.Y., J.M. House, and N.H. Kyong. 2004. Subsystem level fault diagnosis of a building's air-handling unit using general regression neural networks. Applied Energy 77(2):153–70.
   Web of Science ®Google Scholar
• Lianzhong, L., and M. Zaheeruddin. 2014. Fault tolerant control strategies for a high-rise building hot water heating system. Building Services Engineering Research and Technology 35(6):653–670.
   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
• Li, H., and J.E. Braun. 2007a. A methodology for diagnosing multiple simultaneous faults in vapor-compression air conditioners. HVAC&R Research 13(2):369–95.
   Web of Science ®Google Scholar
• Li, H., and J.E. Braun. 2007b. Decoupling features and virtual sensors for diagnosis of faults in vapor compression air conditioners. International Journal of Refrigeration 30(3):546–64.
   Web of Science ®Google Scholar
• Li, S., and J. Wen. 2014a. A model-based fault detection and diagnostic methodology based on PCA method and wavelet transform. Energy and Buildings 68:63–71.
   Web of Science ®Google Scholar
• Li, S., and J. Wen. 2014b. Application of pattern matching method for detecting faults in air handling unit system. Automation in Construction 43:49–58.
   Web of Science ®Google Scholar
• Li, Y. 2012. Electrical signal based fault detection and diagnosis for rooftop units. Dissertation, Electrical engineering, University of Nebraska, Lincoln, NE.
   Google Scholar
• Liang, J., and R. Du. 2007. Model-based fault detection and diagnosis of HVAC systems using support vector machine method. International Journal of Refrigeration 30(6):1104–14.
   Web of Science ®Google Scholar
• Lin, G., and D.E. Claridge. 2015. A temperature-based approach to detect abnormal building energy consumption. Energy and Buildings 93:110–8.
   Web of Science ®Google Scholar
• Liu, D., Q. Chen, K. Mori, and Y. Kida. 2010. A method for detecting abnormal electricity energy consumption in buildings. Journal of Computational Information Systems 6(14):4887–95.
   Google Scholar
• Magoulès, F., H.X. Zhao, and D. Elizondo. 2013. Development of an RDP neural network for building energy consumption fault detection and diagnosis. Energy and Buildings 62:133–8.
   Web of Science ®Google Scholar
• Marino, F., A. Capozzoli, M. Grossoni, F. Lauro, F. Leccese, F. Moretti, S. Panzierei, and S. Pizzuti. 2014. Indoor lighting fault detection and diagnosis using a data fusion approach. Energy Production and Management in the 21st Century: The Quest for Sustainable Energy 190:183.
   Google Scholar
• Mavromatidis, G., S. Acha, and N. Shah. 2013. Diagnostic tools of energy performance for supermarkets using Artificial Neural Network algorithms. Energy and Buildings 62:304–14.
   Web of Science ®Google Scholar
• Mele, F.M. 2012. A model-based approach to HVAC fault detection and diagnosis. Master of Science Thesis, Department of Electrical Engineering, Royal Institute of Technology, Stockholm, Sweden.
   Google Scholar
• Miller, C., Z. Nagy, and A. Schlueter. 2015. Automated daily pattern filtering of measured building performance data. Automation in Construction 49:1–17.
   Web of Science ®Google Scholar
• Müller, T., N. Réhault, and T. Rist. 2013. A qualitative modeling approach for fault detection and diagnosis on HVAC systems. In: International Conference for Enhanced Building Operations 2013, Montreal, Canada, October 8–11.
   Google Scholar
• Najafi, M., D.M. Auslander, P.L. Bartlett, P. Haves, and M.D. Sohn. 2012a. Application of machine learning in the fault diagnostics of air handling units. Applied Energy 96:347–58.
   Web of Science ®Google Scholar
• Najafi, M., D.M. Auslander, P. Haves, and M.D. Sohn. 2012b. A statistical pattern analysis framework for rooftop unit diagnostics. HVAC&R Research 18(3):406–16.
   Web of Science ®Google Scholar
• Namburu, S.M., M.S. Azam, J. Luo, K. Choi, and K.R. Pattipati. 2007. Data-driven modeling, fault diagnosis and optimal sensor selection for HVAC chillers. IEEE Transactions on Automation Science and Engineering 4(3):469–73.
   Web of Science ®Google Scholar
• Narayanaswamy, B., B. Balaji, R. Gupta, and Y. Agarwal. 2014. Data driven investigation of faults in HVAC systems with model, cluster and compare (MCC). In Proceedings of the 1st ACM Conference on Embedded Systems for Energy-Efficient Buildings, New York, NY, November 3–6, ACM; pp. 50–9.
   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
• Navarro-Esbri, J., E. Torrella, and R. Cabello. 2006. A vapor compression chiller fault detection technique based on adaptative algorithms. Application to on-line refrigerant leakage detection. International Journal of Refrigeration 29(5):716–23.
   Web of Science ®Google Scholar
• Noh, J.H., P.C. Joshi, T. Kuruganti, and P.D. Rack. 2015. Pulse thermal processing for low thermal budget integration of IGZO thin film transistors. IEEE Journal of the Electron Devices Society 3(3):297, 301. DOI: 10.1109/JEDS.2014.2376411
   Web of Science ®Google Scholar
• O'Neill, Z., X. Pang, M. Shashanka, P. Haves, and T. Bailey. 2014. Model-based real-time whole building energy performance monitoring and diagnostics. Journal of Building Performance Simulation 7(2):83–99.
   Web of Science ®Google Scholar
• Papale, D. 2012. A model-parameter invariant approach to HVAC fault detection and diagnosis. Master of Science Thesis, Department of Electrical Engineering, Royal Institute of Technology, Stockholm, Sweden.
   Google Scholar
• Ploennigs, J., B. Chen, A. Schumann, and N. Brady. 2013. Exploiting generalized additive models for diagnosing abnormal energy use in buildings. In Proceedings of the 5th ACM Workshop on Embedded Systems for Energy-Efficient Buildings, Rome, Italy, November 11–15, ACM; pp. 1–8.
   Google Scholar
• Prakash, J.A.M. 2006. Energy optimization potential through improved onsite analyzing methods in refrigeration. Master of Science Thesis, Department of Energy Technology, Royal Institute of Technology, Stockholm, Sweden.
   Google Scholar
• Provan, G. 2011. Generating reduced-order diagnosis models for HVAC systems. In Itnl. Workshop on Principles of Diagnosis, Murnau, Germany, October 4–7.
   Google Scholar
• Qin, J., and S. Wang. 2005. A fault detection and diagnosis strategy of VAV air-conditioning systems for improved energy and control performances. Energy and Buildings 37(10):1035–48.
   Web of Science ®Google Scholar
• Radhakrishnan, R., D. Nikovski, K. Peker, and A. Divakaran. 2006. A comparison between polynomial and locally weighted regression for fault detection and diagnosis of HVAC equipment. In IECON 2006-32nd Annual Conference on IEEE Industrial Electronics, Paris, France, November 6–10, IEEE; pp. 3668–73.
   Google Scholar
• Ren, N., J. Liang, B. Gu, and H. Han. 2008. Fault diagnosis strategy for incompletely described samples and its application to refrigeration system. Mechanical Systems and Signal Processing 22(2):436–50.
   Web of Science ®Google Scholar
• Rueda, E., S.A. Tassou, and I.N. Grace. 2005. Fault detection and diagnosis in liquid chillers. Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering 219(2):117–25.
   Web of Science ®Google Scholar
• Schein, J., and S.T. Bushby. 2006. A hierarchical rule-based fault detection and diagnostic method for HVAC systems. HVAC&R Research 12(1):111–25.
   Web of Science ®Google Scholar
• Schein, J., S.T. Bushby, N.S. Castro, and J.M. House. 2006. A rule-based fault detection method for air handling units. Energy and Buildings 38(12):1485–92.
   Web of Science ®Google Scholar
• Seem, J.E. 2007. Using intelligent data analysis to detect abnormal energy consumption in buildings. Energy and Buildings 39(1):52–8.
   Web of Science ®Google Scholar
• Sharifi, R., and D. Dagnachew. 2012. Fault detection in lighting systems: First phase results. Cambridge, MA: Philips Research.
   Google Scholar
• Song, Y.H., Y. Akashi, and J.J. Yee. 2008. A development of easy-to-use tool for fault detection and diagnosis in building air-conditioning systems. Energy and Buildings 40(2):71–82.
   Web of Science ®Google Scholar
• Srivastav, A., A. Tewari, and B. Dong. 2013. Baseline building energy modeling and localized uncertainty quantification using Gaussian mixture models. Energy and Buildings 65:438–47.
   Web of Science ®Google Scholar
• Sterling, R. 2015. Self-aware buildings: an evaluation framework and implementation technologies for improving building operations. Ph.D Thesis, College of Engineering and Informatics, Ireland.
   Google Scholar
• Sterling, R., G. Provan, J. Febres, D. O'Sullivan, P. Struss, and M.M. Keane. 2014. Model-based fault detection and diagnosis of air handling units: A comparison of methodologies. Energy Procedia 62:686–93.
   Google Scholar
• Sun, Biao, P. Luh, Q. Jia, Z. O,Neil, and F. Song. 2014. Building energy doctors: An SPC and Kalman filter-based method for system-level fault detection in HVAC systems. IEEE Transactions on Automation Science and Engineering 11(1):215–29.
   Web of Science ®Google Scholar
• Sutharssan, T., S. Stoyanov, C. Bailey, and Y. Rosunally. 2012. Prognostics and health monitoring of high power led. Micromachines 3(1):78–100.
   Web of Science ®Google Scholar
• Thumati, B.T., M.A. Feinstein, J.W. Fonda, A. Turnbull, F.J. Weaver, M.E. Calkins, and S. Jagannathan. 2011. An online model-based fault diagnosis scheme for HVAC systems. In 2011 IEEE International Conference on Control Applications (CCA), Denver, CO, September 28–30, IEEE; pp. 70–5.
   Google Scholar
• Wang, H., Y. Chen, C.W. Chan, and J. Qin. 2011. A robust fault detection and diagnosis strategy for pressure-independent VAV terminals of real office buildings. Energy and Buildings 43(7):1774–83.
   Web of Science ®Google Scholar
• Wang, H., Y. Chen, C.W. Chan, and J. Qin. 2012. 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, C.W. Chan, J. Qin, and J. Wang. 2012. 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, L., S. Greenberg, J. Fiegel, A. Rubalcava, S. Earni, X. Pang, R. Yin, S. Woodworth, and J. Hernandez-Maldonado. 2013. Monitoring-based HVAC commissioning of an existing office building for energy efficiency. Applied Energy 102:1382–90.
   Web of Science ®Google Scholar
• Wang, S., and F. Xiao. 2004a. Detection and diagnosis of AHU sensor faults using principal component analysis method. Energy Conversion and Management 45(17):2667–86.
   Web of Science ®Google Scholar
• Wang, S., and F. Xiao. 2004b. AHU sensor fault diagnosis using principal component analysis method. Energy and Buildings 36(2):147–60.
   Web of Science ®Google Scholar
• Wang, S., and J. Qin. 2005. Sensor fault detection and validation of VAV terminals in air conditioning systems. Energy Conversion and Management 46(15):2482–500.
   Web of Science ®Google Scholar
• Wang, S., and F. Xiao. 2006. Sensor fault detection and diagnosis of air-handling units using a condition-based adaptive statistical method. HVAC&R Research 12(1):127–50.
   Web of Science ®Google Scholar
• Wang, S., and J. Cui. 2006. A robust fault detection and diagnosis strategy for centrifugal chillers. HVAC&R Research 12(3):407–28.
   Web of Science ®Google Scholar
• Wang, S., Q. Zhou, and F. Xiao. 2010. A system-level fault detection and diagnosis strategy for HVAC systems involving sensor faults. Energy and Buildings 42(4):477–90.
   Web of Science ®Google Scholar
• Weimer, J., S.A. Ahmadi, J. Araujo, F.M. Mele, D. Papale, I. Shames, H. Sandberg, and K.H. Johansson. 2012. Active actuator fault detection and diagnostics in HVAC systems. In Proceedings of the Fourth ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings, Toronto, Canada, November 6, ACM; pp. 107–14.
   Google Scholar
• West, S.R., Y. Guo, X.R. Wang, and J. Wall. 2011. Automated fault detection and diagnosis of HVAC subsystems using statistical machine learning. In 12th International Conference of the International Building Performance Simulation Association, Sydney, Australia.
   Google Scholar
• Wichman, A., and J.E. Braun. 2009. Fault detection and diagnostics for commercial coolers and freezers. HVAC&R Research 15(1):77–99.
   Web of Science ®Google Scholar
• Wu, J.D., and S.Y. Liao. 2010. Fault diagnosis of an automotive air-conditioner blower using noise emission signal. Expert Systems with Applications 37(2):1438–45.
   Web of Science ®Google Scholar
• Wu, S., and J.Q. Sun. 2011a. A top-down strategy with temporal and spatial partition for fault detection and diagnosis of building HVAC systems. Energy and Buildings 43(9):2134–9.
   Web of Science ®Google Scholar
• Wu, S., and J.Q. Sun. 2011b. Cross-level fault detection and diagnosis of building HVAC systems. Building and Environment 46(8):1558–66.
   Web of Science ®Google Scholar
• Xiao, F., S. Wang, and J. Zhang. 2006. A diagnostic tool for online sensor health monitoring in air-conditioning systems. Automation in Construction 15(4):489–503.
   Web of Science ®Google Scholar
• Xiao, F., Y. Zhao, J. Wen, and S. Wang. 2014. Bayesian network based FDD strategy for variable air volume terminals. Automation in Construction 41:106–18.
   Web of Science ®Google Scholar
• Xu, X., F. Xiao, and S. Wang. 2008. Enhanced chiller sensor fault detection, diagnosis and estimation using wavelet analysis and principal component analysis methods. Applied Thermal Engineering 28(2):226–37.
   Web of Science ®Google Scholar
• Yang, H., S. Cho, C.S. Tae, and M. Zaheeruddin. 2008. Sequential rule based algorithms for temperature sensor fault detection in air handling units. Energy Conversion and Management 49(8):2291–306.
   Web of Science ®Google Scholar
• Yang, X.B., X.Q. Jin, Z.M. Du, and Y.H. Zhu. 2011. A novel model-based fault detection method for temperature sensor using fractal correlation dimension. Building and Environment 46(4):970–9.
   Web of Science ®Google Scholar
• Yang, X.B., X.Q. Jin, Z.M. Du, Y.H. Zhu, and Y.B. Guo. 2013. A hybrid model-based fault detection strategy for air handling unit sensors. Energy and Buildings 57:132–43.
   Web of Science ®Google Scholar
• Yiu, J.C.M., and S. Wang. 2007. Multiple ARMAX modeling scheme for forecasting air conditioning system performance. Energy Conversion and Management 48(8):2276–85.
   Web of Science ®Google Scholar
• Yoon, S.H., W.V. Payne, and P.A. Domanski. 2011. Residential heat pump heating performance with single faults imposed. Applied Thermal Engineering 31(5):765–71.
   Web of Science ®Google Scholar
• Yu, D., H. Li, and M. Yang. 2011a. A virtual supply airflow meter in rooftop air conditioning units. Building and Environment 46(6):1292–1302.
   Web of Science ®Google Scholar
• Yu, D., H. Li, and Y. Yu. 2011b. A gray-box based virtual SCFM meter in rooftop air-conditioning units. Journal of Thermal Science and Engineering Applications 3(1):1–7.
   Google Scholar
• Yuwono, M., Y. Guo, J. Wall, J. Li, S. West, G. Platt, and S.W. Su. 2015. Unsupervised feature selection using swarm intelligence and consensus clustering for automatic fault detection and diagnosis in heating ventilation and air conditioning systems. Applied Soft Computing 34:402–25.
   Web of Science ®Google Scholar
• Zhao, X., M. Yang, and H. Li. 2014. Field implementation and evaluation of a decoupling-based fault detection and diagnostic method for chillers. Energy and Buildings 72:419–30.
   Web of Science ®Google Scholar
• Zhou, Q., S. Wang, and Z. Ma. 2009. A model‐based fault detection and diagnosis strategy for HVAC systems. International Journal of Energy Research 33(10):903–18.
   Web of Science ®Google Scholar
• Zhu, Y., X. Jin, and Z. Du. 2012. Fault diagnosis for sensors in air handling unit based on neural network pre-processed by wavelet and fractal. Energy and Buildings 44:7–16.
   Web of Science ®Google Scholar
• Zogg, D., E. Shafai, and H.P. Geering. 2006. Fault diagnosis for heat pumps with parameter identification and clustering. Control Engineering Practice 14(12):1435–44.
   Web of Science ®Google Scholar