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

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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

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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

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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)

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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

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References(48)

References

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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

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通讯作者: 陈斌, bchen63@163.com

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