Indoor Lighting Fault Detection And Diagnosis
Using A Data Fusion Approach
Author(s)
F. Marino, A. Capozzoli, M. Grossoni, F. Lauro, F. Leccese,
F. Moretti, S. Panzierei & S. Pizzuti
Abstract
In this paper, an innovative and automated fault detection and diagnosis (FDD)
approach based on high-level correlation rules in order to improve reliability,
safety and efficiency of a supervised building is presented. The proposed method
is based on the data fusion of different measurements, using their fuzzification
and aggregation through suitable operators, in order to get dimensionless severity
indicators able to diagnose faults and to identify the possible causes (ranked
according their severity) generating them. Thus, a set of possible anomalies that
can occur in a building and the correlation with measured physical quantities
were identified. Experimentation of this FDD technique was applied to indoor
lighting of a real office building. The proposed method was validated over a onemonth
period with the aim of detecting anomalous consumption events,
considering when and in which circumstances they occurred. After this stage, the
FDD system was performed in real time operation.
Keywords: fault detection and diagnosis, smart building, BEMS, data fusion
approach, fuzzy logic, sustainability.
Keywords
fault detection and diagnosis, smart building, BEMS, data fusion
approach, fuzzy logic, sustainability.
Related Book
Other papers in this volume
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