A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 7 Issue 3
Apr.  2020

IEEE/CAA Journal of Automatica Sinica

  • JCR Impact Factor: 11.8, Top 4% (SCI Q1)
    CiteScore: 17.6, Top 3% (Q1)
    Google Scholar h5-index: 77, TOP 5
Turn off MathJax
Article Contents
Panayiotis M. Papadopoulos, Vasso Reppa, Marios M. Polycarpou and Christos G. Panayiotou, "Scalable Distributed Sensor Fault Diagnosis for Smart Buildings," IEEE/CAA J. Autom. Sinica, vol. 7, no. 3, pp. 638-655, May 2020. doi: 10.1109/JAS.2020.1003123
Citation: Panayiotis M. Papadopoulos, Vasso Reppa, Marios M. Polycarpou and Christos G. Panayiotou, "Scalable Distributed Sensor Fault Diagnosis for Smart Buildings," IEEE/CAA J. Autom. Sinica, vol. 7, no. 3, pp. 638-655, May 2020. doi: 10.1109/JAS.2020.1003123

Scalable Distributed Sensor Fault Diagnosis for Smart Buildings

doi: 10.1109/JAS.2020.1003123
Funds:  This work was supported by the European Union’s Horizon 2020 Research and Innovation Programme (739551) (KIOS CoE)
More Information
  • The enormous energy use of the building sector and the requirements for indoor living quality that aim to improve occupants’ productivity and health, prioritize Smart Buildings as an emerging technology. The Heating, Ventilation and Air-Conditioning (HVAC) system is considered one of the most critical and essential parts in buildings since it consumes the largest amount of energy and is responsible for humans comfort. Due to the intermittent operation of HVAC systems, faults are more likely to occur, possibly increasing eventually building’s energy consumption and/or downgrading indoor living quality. The complexity and large scale nature of HVAC systems complicate the diagnosis of faults in a centralized framework. This paper presents a distributed intelligent fault diagnosis algorithm for detecting and isolating multiple sensor faults in large-scale HVAC systems. Modeling the HVAC system as a network of interconnected subsystems allows the design of a set of distributed sensor fault diagnosis agents capable of isolating multiple sensor faults by applying a combinatorial decision logic and diagnostic reasoning. The performance of the proposed method is investigated with respect to robustness, fault detectability and scalability. Simulations are used to illustrate the effectiveness of the proposed method in the presence of multiple sensor faults applied to a 83-zone HVAC system and to evaluate the sensitivity of the method with respect to sensor noise variance.

     

  • loading
  • [1]
    N. E. Klepeis, W. C. Nelson, W. R. Ott, J. P. Robinson, A. M. Tsang, P. Switzer, J. V. Behar, S. C. Hern, and W. H. Engelmann, “The National Human Activity Pattern Survey (NHAPS): a resource for assessing exposure to environmental pollutants,” J. Exposure Analysis and Environmental Epidemiology, vol. 11, no. 3, pp. 231–252, 2001. doi: 10.1038/sj.jea.7500165
    [2]
    J. Sun and Y. Zhang, “Towards an energy efficient architecture in smart building,” in Proc. Int. Conf. Computational Intelligence and Communication Networks, 2015, pp. 1589−1592.
    [3]
    A. Capozzoli, F. Lauro, and I. Khan, “Fault detection analysis using data mining techniques for a cluster of smart office buildings,” Expert Systems with Applications, vol. 42, no. 9, pp. 4324–4338, 2015. doi: 10.1016/j.eswa.2015.01.010
    [4]
    G. Boracchi, M. Michaelides, and M. Roveri, “Detecting contaminants in smart buildings by exploiting temporal and spatial correlation,” in Proc. IEEE Symp. Series Computational Intelligence, 2015, pp. 601−608.
    [5]
    M. Pǎtrașcu and M. Drǎgoicea, “Integrating agents and services for control and monitoring: managing emergencies in smart buildings,” in Proc. 3rd Int. Workshop Service Orientation in Holonic and Multiagent Manufacturing and Robotics, 2013, vol. 544, pp. 209−224.
    [6]
    M. Kintner-Meyer, M. R. Brambley, T. Carlon, and N. Bauman, “Wireless sensors: technology and cost-savings for commercial buildings,” Teaming for Efficiency:Proc. the ACEEE Summer Study on Energy Efficiency in Buildings, vol. 7, no. 8, pp. 121–134, 2002.
    [7]
    R. Isermann, Fault-Diagnosis Systems: An Introduction From Fault Detection to Fault Tolerance. Springer Science & Business Media, 2006.
    [8]
    J. Schein, S. T. Bushby, N. S. Castro, and J. M. House, “A rule-based fault detection method for air handling units,” Energy and Buildings, vol. 38, no. 12, pp. 1485–1492, 2006. doi: 10.1016/j.enbuild.2006.04.014
    [9]
    H. Yang, S. Cho, C.-S. Tae, and M. Zaheeruddin, “Sequential rule based algorithms for temperature sensor fault detection in air handling units,” Energy Conversion and Management, vol. 49, no. 8, pp. 2291–2306, 2008. doi: 10.1016/j.enconman.2008.01.029
    [10]
    Y. Zhao, J. Wen, and S. Wang, “Diagnostic Bayesian networks for diagnosing air handling units faults - part II: faults in coils and sensors,” Applied Thermal Engineering, vol. 90, pp. 145–157, 2015. doi: 10.1016/j.applthermaleng.2015.07.001
    [11]
    M. Sampath, R. Sengupta, S. Lafortune, and K. Sinnamohideen, “Failure diagnosis using discrete-event models,” IEEE Trans. Control Systems Technology, vol. 4, no. 2, pp. 105–124, 1996. doi: 10.1109/87.486338
    [12]
    S. Katipamula and M. R. Brambley, “Review article: methods for fault detection, diagnostics, and prognostics for building systemsa review, part I,” HVAC&R Research, vol. 11, no. 1, pp. 3–25, 2005. doi: 10.1080/10789669.2005.10391123
    [13]
    S. Wang and F. Xiao, “AHU sensor fault diagnosis using principal component analysis method,” Energy and Buildings, vol. 36, no. 2, pp. 147–160, 2004. doi: 10.1016/j.enbuild.2003.10.002
    [14]
    Z. Du and X. Jin, “Detection and diagnosis for sensor fault in HVAC systems,” Energy Conversion and Management, vol. 48, no. 3, pp. 693–702, 2007. doi: 10.1016/j.enconman.2006.09.023
    [15]
    M. Kumar and I. N. Kar, “Fault detection and diagnosis of airconditioning systems using residuals,” in Proc. 10th IFAC Int. Symp. Dynamics and Control of Process Systems, 2013, pp. 607−612.
    [16]
    A. Beghi, L. Cecchinato, L. Corso, M. Rampazzo, and F. Simmini, “Process history-based fault detection and diagnosis for VAVAC systems,” in Proc. IEEE Int. Conf. Control Applications, 2013, pp. 1165–1170.
    [17]
    J. Liang and R. Du, “Model-based fault detection and diagnosis of HVAC systems using support vector machine method,” Int. Journal of Refrigeration, vol. 30, no. 6, pp. 1104–1114, 2007. doi: 10.1016/j.ijrefrig.2006.12.012
    [18]
    T. Mulumba, A. Afshari, K. Yan, W. Shen, and L. K. Norford, “Robust model-based fault diagnosis for air handling units,” Energy and Buildings, vol. 86, pp. 698–707, 2015. doi: 10.1016/j.enbuild.2014.10.069
    [19]
    S. Wang and Y. Chen, “Fault-tolerant control for outdoor ventilation air flow rate in buildings based on neural network,” Building and Environment, vol. 37, no. 7, pp. 691–704, 2002. doi: 10.1016/S0360-1323(01)00076-2
    [20]
    Z. Du, X. Jin, and Y. Yang, “Fault diagnosis for temperature, flow rate and pressure sensors in VAV systems using wavelet neural network,” Applied Energy, vol. 86, no. 9, pp. 1624–1631, 2009. doi: 10.1016/j.apenergy.2009.01.015
    [21]
    S. Wang and J.-B. Wang, “Robust sensor fault diagnosis and validation in HVAC systems,” Trans. of Institute of Measurement and Control, vol. 24, no. 3, pp. 231–262, 2002. doi: 10.1191/0142331202tm030oa
    [22]
    M. Padilla, D. Choinière, and J. A. Candanedo, “A model-based strategy for self-correction of sensor faults in variable air volume air handling units,” Science and Technology for the Built Environment, vol. 21, no. 7, pp. 1018–1032, 2015. doi: 10.1080/23744731.2015.1025682
    [23]
    X. F. Liu and A. Dexter, “Fault-tolerant supervisory control of VAV airconditioning systems,” Energy and Buildings, vol. 33, no. 4, pp. 379–389, 2001. doi: 10.1016/S0378-7788(00)00120-1
    [24]
    C. H. Lo, P. T. Chan, Y. K. Wong, a. B. Rad, and K. L. Cheung, “Fuzzy genetic algorithm for automatic fault detection in HVAC systems,” Applied Soft Computing J., vol. 7, no. 2, pp. 554–560, 2007. doi: 10.1016/j.asoc.2006.06.003
    [25]
    H. Yoshida, S. Kumar, and Y. Morita, “Online fault detection and diagnosis in VAV air handling unit by RARX modeling,” Energy and Buildings, vol. 33, no. 4, pp. 391–401, 2001. doi: 10.1016/S0378-7788(00)00121-3
    [26]
    J. C. M. Yiu and S. Wang, “Multiple ARMAX modeling scheme for forecasting air conditioning system performance,” Energy Conversion and Management, vol. 48, no. 8, pp. 2276–2285, 2007. doi: 10.1016/j.enconman.2007.03.018
    [27]
    W. J. Turner, A. Staino, and B. Basu, “Residential HVAC fault detection using a system identification approach,” Energy and Buildings, vol. 151, pp. 1–17, 2017. doi: 10.1016/j.enbuild.2017.06.008
    [28]
    X. B. Yang, X. Q. Jin, Z. M. Du, Y. H. Zhu, and Y. B. Guo, “A hybrid model-based fault detection strategy for air handling unit sensors,” Energy and Buildings, vol. 57, pp. 132–143, 2013. doi: 10.1016/j.enbuild.2012.10.048
    [29]
    M. Bonvini, M. D. Sohn, J. Granderson, M. Wetter, and M. A. Piette, “Robust on-line fault detection diagnosis for HVAC components based on nonlinear state estimation techniques,” Applied Energy, vol. 124, pp. 156–166, 2014. doi: 10.1016/j.apenergy.2014.03.009
    [30]
    B. T. Thumati, M. A. Feinstein, J. W. Fonda, A. Turnbull, F. J. Weaver, M. E. Calkins, and S. Jagannathan, “An online model-based fault diagnosis scheme for HVAC systems,” in Proc. IEEE Int. Conf. Control Applications, 2011, pp. 70−75.
    [31]
    P. M. Papadopoulos, V. Reppa, M. M. Polycarpou, and C. G. Panayiotou, “Distributed diagnosis of actuator and sensor faults in HVAC systems,” in Proc. 20th IFAC World Congr., 2017, pp. 4293−4293.
    [32]
    H. Shahnazari, P. Mhaskar, J. M. House, and T. I. Salsbury, “Modeling and fault diagnosis design for HVAC systems using recurrent neural networks,” Computers and Chemical Engineering, vol. 216, pp. 189–203, 2019.
    [33]
    Y. Chen and L. Lan, “Fault detection, diagnosis and data recovery for a real building heating/cooling billing system,” Energy Conversion and Management, vol. 51, no. 5, pp. 1015–1024, 2010. doi: 10.1016/j.enconman.2009.12.004
    [34]
    R. Yan, Z. J. Ma, G. Kokogiannakis, and Y. Zhao, “A sensor fault detection strategy for air handling units using cluster analysis,” Automation in Construction, vol. 70, no. 1, pp. 77–88, 2016. doi: 10.1016/j.autcon.2016.06.005
    [35]
    Q. Zhou, S. W. Wang, and Z. J. Ma, “A model-based fault detection and diagnosis strategy for HVAC systems,” Int. J. Energy Research, vol. 33, no. 10, pp. 903–918, 2009.
    [36]
    S. Wang, Q. Zhou, and F. Xiao, “A system-level fault detection and diagnosis strategy for HVAC systems involving sensor faults,” Energy and Buildings, vol. 42, no. 4, pp. 477–490, 2010. doi: 10.1016/j.enbuild.2009.10.017
    [37]
    D. Sklavounos, E. Zervas, O. Tsakiridis, and J. Stonham, “A subspace identification method for detecting abnormal behavior in HVAC systems,” J. Energy, vol. 2015, pp. 1−12, 2015.
    [38]
    V. Gunes, S. Peter, and T. Givargis, “Improving energy efficiency and thermal comfort of smart buildings with HVAC systems in the presence of sensor faults,” in Proc. 17th IEEE Int. Conf. High Performance Computing and Communications, 7th Int. Symp. Cyberspace Safety and Security, and 12th Int. Conf. Embedded Software and Systems, 2015, pp. 945−950.
    [39]
    V. Reppa, P. Papadopoulos, M. M. Polycarpou, and C. G. Panayiotou, “Distributed detection and isolation of sensor faults in HVAC systems,” in Proc. Mediterranean Conf. Control and Automation, 2013, pp. 401−406.
    [40]
    P. M. Papadopoulos, V. Reppa, M. M. Polycarpou, and C. G. Panayiotou, “Distributed adaptive estimation scheme for isolation of sensor faults in multi-zone HVAC systems,” in Proc. 9th IFAC Symp. Fault Detection, Supervision and Safety for Technical Processes, 2015, pp. 1146−1151.
    [41]
    V. Reppa, P. Papadopoulos, M. M. Polycarpou, and C. G. Panayiotou, “A distributed architecture for HVAC sensor fault detection and isolation,” IEEE Trans. Control Systems Technology, vol. 23, no. 4, pp. 1323–1337, Jul. 2015. doi: 10.1109/TCST.2014.2363629
    [42]
    P. M. Papadopoulos, V. Reppa, M. M. Polycarpou, and C. G. Panayiotou, “Distributed adaptive sensor fault tolerant control for smart buildings,” in Proc. 54th IEEE Conf. Decision and Control, 2015, pp. 3143−3148.
    [43]
    V. Reppa, M. M. Polycarpou, and C. G. Panayiotou, “Sensor Fault Diagnosis,” Foundations and Trends in Systems and Control, vol. 3, no. 1–2, pp. 1–248, 2016. doi: 10.1561/2600000007
    [44]
    S. Riverso, F. Boem, G. Ferrari-Trecate, and T. Parisini, “Fault diagnosis and control-reconfiguration in large-scale systems : a plug-andplay approach,” in Proc. IEEE Conf. Decision and Control, 2014, pp. 4977−4982.
    [45]
    V. Reppa, P. Papadopoulos, M. M. Polycarpou, and C. G. Panayiotou, “A distributed virtual sensor scheme for smart buildings based on adaptive approximation,” in Proc. IEEE Int. Joint Conf. Neural Networks, 2014, pp. 99−106.
    [46]
    M. Zaheer-Uddin, “Temperature control of multizone indoor spaces based on forecast and actual loads,” Building and Environment, vol. 29, no. 4, pp. 485–493, 1994. doi: 10.1016/0360-1323(94)90007-8
    [47]
    M. Zaheer-Uddin and R. V. Patel, “Optimal tracking control of multizone indoor environmental spaces,” J. Dynamic Systems,Measurement,and Control, vol. 117, no. 3, pp. 292–303, 1995. doi: 10.1115/1.2799119
    [48]
    E. Witrant, S. Mocanu, O. Sename, and Others, “A hybrid model and MIMO control for intelligent buildings temperature regulation over WSN,” in Proc. 8th IFAC Workshop Time Delay Systems, 2009, pp. 420−425.

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(8)  / Tables(6)

    Article Metrics

    Article views (2745) PDF downloads(235) Cited by()

    Highlights

    • Fault diagnosis for energy systems.
    • Distributed fault diagnosis for smart buildings.
    • Formulation of complex HVAC building systems as a network of strongly interconnected subsystems.
    • Design of a distributed, model-based algorithm for sensor fault detection and isolation in large-scale HVAC systems.
    • Sensor fault diagnosis algorithm enhanced with robustness, improved detectability and scalability.

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return