A cognitive monitoring system for contaminant detection in intelligent buildings

Intelligent buildings are equipped with sensing systems able to measure the contaminant concentration in the different building zones for safety purposes. The aim of these systems is to promptly detect the presence of a contaminant so that appropriate actions can be taken to ensure the safety of the people. At the same time, these sensing systems, which operate in real-world conditions, suffer from noise and sensor degradation faults. Both noise and faults can induce false alarms (resulting in unnecessary disruptive actions such as building evacuation) or missed alarms (when the presence of a contaminant is not detected). This paper proposes a novel cognitive monitoring system for performing contaminant detection in intelligent buildings with real-time point-trigger sensors. The proposed system reduces the occurrence of false alarms by means of a three-layered architecture, which employs cognitive mechanisms to validate possible detections and discriminate between the presence of a real contaminant source and a degradation fault affecting the sensors of the sensing system. In addition, the proposed system is able to isolate the building zone containing the contaminant source (or the faulty sensor) and estimate the onset time of the release (or the fault).