A Cognitive Monitoring System for Detecting and Isolating Contaminants and Faults in Intelligent Buildings

Intelligent buildings are typically endowed with sensing devices that are able to measure the concentration of specific contaminants in relevant zones. The collected measurements are subsequently processed by intelligent algorithms in order to enable the prompt detection and isolation of contaminant sources inside the building. Unfortunately, in real-world conditions, these sensing devices may suffer from faults affecting the sensors or the embedded electronics. Such faults, generally result in perturbed or missed data in the acquired data-stream, that can induce false alarms (or possibly missed alarms) and compromise the contaminant detection and isolation ability. This paper proposes a three-layer cognitive monitoring system for the detection and isolation of both contaminants and sensor faults in intelligent buildings. The first two layers are designed for the prompt detection of small variations in the concentration of a specific contaminant, while reducing the possible occurrence of false alarms. At the third layer, a cognitive mechanism employing a propagation model for the contaminant, which is based on the airflows between the building zones, allows to isolate the source zone and discriminate between sensor faults and the presence of a contaminant source. The proposed method is validated using a realistic 14-zone building scenario.