Fault Isolability Analysis and Optimal Sensor Placement for Fault Diagnosis in Smart Buildings

Max Emil S. Trothe, Hamid Reza Shaker, Muhyiddine Jradi and Krzysztof Arendt
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Max Emil S. Trothe: Center for Energy Informatics, The Maersk Mc Kinney Moller Institute, University of Southern Denmark, 5230 Odense, Denmark
Hamid Reza Shaker: Center for Energy Informatics, The Maersk Mc Kinney Moller Institute, University of Southern Denmark, 5230 Odense, Denmark
Muhyiddine Jradi: Center for Energy Informatics, The Maersk Mc Kinney Moller Institute, University of Southern Denmark, 5230 Odense, Denmark
Krzysztof Arendt: Center for Energy Informatics, The Maersk Mc Kinney Moller Institute, University of Southern Denmark, 5230 Odense, Denmark

Energies, 2019, vol. 12, issue 9, 1-12

Abstract: Faults and anomalies in buildings are among the main causes of building energy waste and occupant discomfort. An effective automatic fault detection and diagnosis (FDD) process in buildings can therefore save a significant amount of energy and improve the comfort level. Fault diagnosability analysis and an optimal FDD-oriented sensor placement are prerequisites for effective, efficient and successful diagnostics. This paper addresses the problem of fault diagnosability for smart buildings. The method used in the paper is a model-based technique which uses Dulmage-Mendelsohn decomposition. To the best of our knowledge, this is the first time that this method is used for applications in smart buildings. First a dynamic model for a zone in a real-case building is developed in which faults are also introduced. Then fault diagnosability is investigated by analyzing the fault isolability of the model. Based on the investigation, it was concluded that not all the faults in the model are diagnosable. Then an approach for placing new sensors is implemented. It is observed that for two test scenarios, placing additional sensors in the model leads to full diagnosability. Since sensors placement is key for an effective FDD process, the optimal placement of such sensors is also studied in this work. A case study of campus building OU44 at the University of Southern Denmark is considered. The results show that as the system gets more complicated by introducing more faults, additional sensors should be added to achieve full diagnosability.

Keywords: fault diagnosability; sensor placement; smart buildings; Dulmage-Mendelsohn decomposition (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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