Paper Info
Title
Scalable distributed sensor fault diagnosis for smart buildings
Abstract
The enormous energy use of the building sector and the requirements for indoor living quality that aim to improve occupants ʼ 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 ʼ 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.
Year
DOI
Venue
2020
10.1109/JAS.2020.1003123
IEEE/CAA Journal of Automatica Sinica
Keywords
DocType
Volume
Building automation,fault diagnosis,fault location,smart homes
Journal
7
Issue
ISSN
Citations 
3
2329-9266
1
PageRank 
References 
Authors
0.35
0
4
Name
Order
Citations
PageRank
Panayiotis M. Papadopoulos110.35
Vasso Reppa2517.18
Marios Polycarpou32020206.96
Christos G. Panayiotou447258.98