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Fault diagnosis for air contamination events in three-dimensional environments

We propose an Extended Implicit Kalman Filter for fault diagnosis of air contaminants in three-dimensional environments. In order to accurately detect and diagnose air contamination faults in environments such as a space station, we combine the use of an Extended Implicit Kalman Filter with the use of sensitivity matrices that provide the filter with an initial guess of the capacity and location of the contamination source. We show that this hybrid system works well in simulations for obtaining an optimal solution to the ill-posed inverse problem of determining the location and strength of an unknown source emission in a space station cabin.