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The behavior of multi-component engineered systems is typically characterized by transitions among discrete modes of operation and failure, each one giving rise to a specific continuous dynamics of evolution. The detection of the system's mode change time represents a particularly challenging task because it requires keeping track of the transitions among the multiple system dynamics corresponding to the different modes of operation and failure. To this purpose, we implement a novel particle filtering method within a log-likelihood ratio approach here, specifically tailored to handle hybrid dynamic systems. The proposed method relies on the generation of multiple particle swarms for each discrete mode, each originating from the nominal particle swarm at different time instants. The hybrid system considered consists of a hold up tank filled with liquid, whose level is autonomously maintained between two thresholds; the system behavior is controlled by discrete mode actuators whose states are estimated by a Monte Carlo-based particle filter on the basis of noise level, and temperature measurements.