Scalable Distributed Sensor Fault Diagnosis for Smart Buildings

Panayiotis M. Papadopoulos; Vasso Reppa; Marios M. Polycarpou; Christos G. Panayiotou

doi:10.1109/JAS.2020.1003123

Volume 7 Issue 3

Apr. 2020

IEEE/CAA Journal of Automatica Sinica

JCR Impact Factor: 15.3, Top 1 (SCI Q1)

CiteScore: 23.5, Top 2% (Q1)
Google Scholar h5-index: 77， TOP 5

Turn off MathJax

Article Contents

Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2020 > 7(3): 638-655

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

Citation:

PDF( 2394 KB)

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

Author Bio:
Panayiotis M. Papadopoulos (S’13) received the B.Sc. and M.Sc. degrees from the University of Cyprus, Cyprus, in 2012 and 2014, respectively, where he is currently pursuing the Ph.D. degree, all in electrical engineering. His current research interests include fault diagnosis for distributed systems, nonlinear control theory, intelligent systems, and adaptive fault-tolerant control in HVAC Systems. He is a Student Member of ASHRAE and Chair of IEEE University of Cyprus Student Branch from Feb. 2015 to Feb. 2018. He has been awarded an Honorable Mention as one of the finalist papers for the Young Author Award presented at the 10th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes, SAFEPROCESS 2018

Vasso Reppa (M’06) has been an Assistant Professor in the Department of Maritime and Transport Technology of Delft University of Technology, The Netherlands since 2018. She obtained the doctorate (2010) in electrical and computer engineering from the University of Patras, Greece. In 2009 she joined IBM Zurich Research Laboratory in Switzerland as a student intern. From 2011 to 2017, she was a Research Associate (now Research Affiliate) with the KIOS Research and Innovation Center of Excellence in Cyprus. In 2013, Dr. Reppa was awarded the Marie Curie Intra European Fellowship and worked as a Research Fellow in CentraleSupelec of the University Paris-Saclay, France from 2014 to 2016. She was a Visiting Researcher at Imperial College, the UK and at University of Newcastle, Australia in 2015 and 2016, respectively. Her research interests include distributed fault diagnosis and fault tolerant control, adaptive learning, observer-based estimation, and applications of autonomous systems in transport, smart buildings, and robotics. Dr Reppa has led the implementation of work packages of several research and development projects (e.g., FP7, INTERREG, H2020, NWO)

Marios M. Polycarpou (F’06) is a Professor of electrical and computer engineering and the Director of the KIOS Research and Innovation Center of Excellence at the University of Cyprus, Cyprus. He received the B.A. degree in computer science and the B.Sc. in electrical engineering, both from Rice University, USA in 1987, and the M.S. and Ph.D. degrees in electrical engineering from the University of Southern California, USA, in 1989 and 1992, respectively. His teaching and research interests include intelligent systems and networks, adaptive and learning control systems, computational intelligence, fault diagnosis, and critical infrastructure systems. Dr. Polycarpou has published more than 350 articles in refereed journals, edited books, and refereed conference proceedings, and co-authored 7 books. He is also the Holder of 6 patents. Prof. Polycarpou is a Fellow of IEEE and IFAC. He was the Recipient of the 2016 IEEE Neural Networks Pioneer Award and the 2014 Best Paper Award for the journal Building and Environment (Elsevier). Prof. Polycarpou served as the President of the IEEE Computational Intelligence Society (2012–2013), the President of the European Control Association (2017–2019), and the Editor-in-Chief of the IEEE Transactions on Neural Networks and Learning Systems (2004–2010). He has participated in more than 70 research projects/grants, funded by several agencies and industry in Europe and the United States, including the prestigious European Research Council (ERC) Advanced Grant and the EU Teaming project. Prof. Polycarpou is an elected Founding Member of the Cyprus Academy of Sciences, Letters, and Arts

Christos G. Panayiotou (SM’10) is a Professor with the Electrical and Computer Engineering (ECE) Department at the University of Cyprus, Cyprus. He is also the Deputy Director of the KIOS Research and Innovation Center of Excellence for which he is also a Founding Member. Christos has received the B.Sc. and a Ph.D. degrees in electrical and computer engineering from the University of Massachusetts, USA, in 1994 and 1999, respectively. He also received the MBA from the Isenberg School of Management, at the aforementioned university in 1999. Before joining the University of Cyprus in 2002, he was a Research Associate at the Center for Information and System Engineering (CISE) and the Manufacturing Engineering Department at Boston University, USA (1999–2002). His research interests include modeling, control, optimization and performance evaluation of discrete event and hybrid systems, cyber-physical systems, event detection and localization, fault diagnosis, smart camera networks, wireless, Ad-hoc and sensor networks, resource allocation, intelligent transportation networks, and intelligent buildings. Christos has published more than 230 papers in international refereed journals and conferences and is the Recipient of the 2014 Best Paper Award for the journal Building and Environment (Elsevier). He is an Associate Editor for the IEEE Transactions of Intelligent Transportation Systems, the IEEE Transactions on Control Systems Technology, the Conference Editorial Board of the IEEE Control Systems Society, the Journal of Discrete Event Dynamical Systems, and the European Journal of Control. He held several positions in organizing committees and technical program committees of numerous international conferences. He has also served as Chair of various subcommittees of the Education Committee of the IEEE Computational Intelligence Society
Corresponding author: P. M. Papadopoulos, M. M. Polycarpou, and C. G. Panayiotou are with the KIOS Research and Innovation Center of Excellence, Department of Electrical and Computer Engineering, University of Cyprus, Nicosia 1678, Cyprus (e-mail: papadopoulos.panagiotis@ucy.ac.cy; mpolycar@ucy.ac.cy; christosp@ucy.ac.cy)
Received Date: 2019-12-10
Revised Date: 2020-01-17
Accepted Date: 2020-02-19

Available Online: 2020-03-27

Abstract

Abstract

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.
- Building automation,
- fault diagnosis,
- fault location,
- smart homes

FullText(HTML)

References(48)

References

[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.

Supplements(0)

Cited By

Proportional views

Proportional views

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

Figures(8) / Tables(6)

Get Citation

PDF

XML

Article Metrics

Article views (2892) PDF downloads(237)

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.

Scalable Distributed Sensor Fault Diagnosis for Smart Buildings

doi: 10.1109/JAS.2020.1003123

Abstract

References

Proportional views

Catalog

通讯作者: 陈斌, bchen63@163.com

Article Metrics

Highlights

Export File

Citation

Format

Content