Scalable Distributed Sensor Fault Diagnosis for Smart Buildings Panayiotis M. Papadopoulos, Student Member, IEEE, Vasso Reppa, Member, IEEE, Marios M. Polycarpou, Fellow, IEEE, and Christos G. Panayiotou, Senior Member, IEEE Abstract —The enormous energy use of the building sector and the requirements for indoor living quality that aim to improve oc- cupants’ productivity and health, prioritize Smart Buildings as an emerging technology. The Heating, Ventilation and Air-Condi- tioning (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 in- termittent operation of HVAC systems, faults are more likely to occur, possibly increasing eventually building’s energy consump- tion 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 dis- tributed 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 sub- systems allows the design of a set of distributed sensor fault dia- gnosis agents capable of isolating multiple sensor faults by apply- ing 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. Index Terms—Building automation, fault diagnosis, fault location, smart homes.   I. Introduction   A. Motivation A CCORDING to the National Human Activity Pattern Survey (NHAPS), an average person in USA spends 86.9% of his/her life indoors [1]. The motivation for the de- velopment of smart buildings is the need to increase the en- ergy efficiency of buildings [2], and the reliability of building’s automation process [3], while decreasing the risk of safety- critical conditions [4], [5]. Since humans spend so much of their time indoors, health and living quality highly depends on the indoor conditions related to humidity, temperature, qual- ity of air and many more. These factors are closely related to the safe and reliable operation of the Heating, Ventilation and Air-Conditioning (HVAC) system. HVAC systems are complex machines that consist of a large number of interconnected components. Faults in electrical and mechanical equipment such as sensors, wires, fans, valves, pumps of the HVAC system are inevitable due to its continual operation. Faults can increase the energy consumption and create discomfort conditions for occupants. The feedback control performance of the HVAC system depends on the availability and reliability of sensor measurements. Advances in wireless network technology and Internet of Things (IoT) technology have enhanced the availability of data in buildings [6]. However, data unreliability due to potential sensor faults can be a major drawback for the performance of the feedback control process.   B. Literature Survey Fault diagnosis is a well-established procedure to discover anomalies in systems [7]. Currently, the industry of HVAC control systems uses ruled-based algorithms to diagnose anomalies during the operation of HVAC systems. The rules are formed by comparing sensor data or relations of sensor data with predefined constant thresholds obtained by experts; sometimes these are also called expert systems. Some examples of ruled-based fault diagnosis schemes for HVAC systems are: 1) the performance assessment rules that identify the mode of operation using specific relationships of measured information [8], [9]; and 2) the cause-effect graphs where the various operation modes of the system (both healthy and faulty modes) are represented as discrete events [10], [11]. The main weaknesses of rule-based fault diagnosis methods are that they are very specific to the system, can fail beyond the boundaries of the expertise incorporated in them, and are difficult to update [12]; in other words, ruled-based fault diagnosis methods do not employ any adaptability when the algorithm is applied to HVAC systems and buildings with different properties and/or parameters. Intelligent fault diagnosis algorithms can be divided into two categories; data-driven/data-mining and model-based fault diagnosis algorithms. The former category includes traditional computational intelligence algorithms that originate from Manuscript received December 10, 2019; revised January 17, 2020; accep- ted February 19, 2020. This work was supported by the European Union’s Horizon 2020 Research and Innovation Programme (739551) (KIOS CoE). Recommended by Associate Editor Chengdong Li. (Corresponding author: Panayiotis M. Papadopoulos.) Citation: P. M. Papadopoulos, V. Reppa, M. M. Polycarpou, and C. G. Panayiotou, “Scalable distributed sensor fault diagnosis for smart buildings,” IEEE/CAA J. Autom. Sinica, vol. 7, no. 3, pp. 638–655, May 2020. P. M. Papadopoulos, M. M. Polycarpou, and C. G. Panayiotou are with the KIOS Research and Innovation Center of Excellence, Department of Electric- al and Computer Engineering, University of Cyprus, Nicosia 1678, Cyprus (e- mail: papadopoulos.panagiotis@ucy.ac.cy; mpolycar@ucy.ac.cy; christosp@ ucy.ac.cy). V. Reppa is with the Department of Maritime and Transport Technology, Delft University of Technology, Delft, 2628 CD, The Netherlands (e-mail: v.reppa@tudelft.nl). Color versions of one or more of the figures in this paper are available on- line at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/JAS.2020.1003123 638 IEEE/CAA JOURNAL OF AUTOMATICA SINICA, VOL. 7, NO. 3, MAY 2020