Combining contaminant event diagnosis with data validation/reconstruction: Application to smart buildings

In this work, a combined sensor data validation/reconstruction and contaminant event diagnosis approach is proposed for Smart Building systems, implemented as a two-stage approach. In the first stage, sensor communication faults are detected and missing data is estimated, in order to provide a reliable dataset to perform contaminant event diagnosis in the second stage. For the first stage, the sensor validation and reconstruction technique is based on the combined use of spatial and time series models. On the one hand, spatial models take advantage of the physical relation between different variables in the system, whilst on the other hand, time series models take advantage of the temporal redundancy of the measured variables, using Holt-Winters time series models. For the second stage, contaminant event diagnosis is based on contaminant detection and isolation estimator schemes, using adaptive thresholds by assuming certain bounds on the measurement noise and the model uncertainty. In order to apply these diagnosis schemes, state-space models have been considered in order to model the contaminant dispersion over the indoor building environment, where the contaminant event is modelled as a fault in the process which needs to be detected and isolated. Finally, the proposed approach is successfully demonstrated for the Holmes House smart building scenario.

I. Introduction

A Smart Building (SB) incorporates computer technology to autonomously govern and adapt the building environment in order to enhance operational and energy efficiency, cost effectiveness, improve users' comfort, productivity and safety, and increase robustness and reliability [1], [2]. The dispersion of contaminants from sources (events) inside a building can compromise the indoor air quality and influence the occupants' comfort, health, productivity and safety. These events could be the result of an accident, faulty equipment or a planned attack. Distributed sensor networks have been widely used in buildings to monitor indoor environmental conditions such as air temperature, humidity and contaminant concentrations. Real-time collected data can be used to alert occupants and/or control environmental conditions. Accurate and prompt identification of contaminant sources can help determining appropriate control solutions such as: (i) indicating safe rescue pathways and/or refugee spaces, (ii) isolating contaminated spaces and (iii) cleaning contaminant spaces by removing sources, ventilating and filtering air. Therefore, the accurate and prompt identification of contaminant sources is an essential part of the SB design.