Decentralized Inverse Model for Estimating Building Fire Source Location and Intensity

A decentralized inverse approach based on Bayesian inference and Markov Chain Monte Carlo sampling is developed to estimate the location and intensity of building fires. Compared with the methods based only on sensor data or fire models, this approach has little limits on the complexity of building structures and can be performed in real time due to the combination of those two methods. Additionally, by dividing the building into several zones, each of which is capable of fire inversion based on its local sensor reading and/or together with neighbor sensor readings, the decentralized algorithm is more efficient and robust compared with the centralized model using all sensor data. Three cases are designed to test the model and investigate the influence of sensor data quality and quantity in a multiroom building. The estimation from the fire zone can match the actual values when sufficient sensor data are used. The closer a zone is to the fire source, the more accurate the result is. In addition, this paper...

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