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DOI

Model-based fault diagnosis has been indicated as a fundamental method in enabling optimal maintenance of buildings, thus leading to important energy savings. However, accurate models of buildings and their technical equipment are seldom available, and this lack of knowledge applies even more to descriptions of modeling and measurement uncertainties, which are needed for developing robust fault detection thresholds with given performances in terms of False Alarm Rates. In the present paper we propose to overcome both limitations, by introducing: 1) a methodology for the automatic synthesis of a global model of a building and its equipment, leveraging a purposely built ontology-based Building Information Model; and 2) a novel message passing algorithm called MP-BUP for automatically propagating the effects of uncertainties in interconnected bilinear systems and derive robust probabilistic thresholds.

Original languageEnglish
Pages (from-to)4184-4190
JournalIFAC-PapersOnLine
Volume50
Issue number1
DOIs
Publication statusPublished - 2017
Event20th World Congress of the International Federation of Automatic Control (IFAC), 2017 - Toulouse, France
Duration: 9 Jul 201714 Jul 2017
Conference number: 20
https://www.ifac2017.org

    Research areas

  • Building automation, Building information model, diagnosis, Fault detection, Lumped-parameter modeling

ID: 47485704