Hybrid Particle Petri Net-Based Prognosis of a Planetary Rover

Abstract

This paper describes a model-based prognosis method for the health management of a planetary rover. Using a hybrid model of the rover, including a continuous part and a discrete part, a prognoser is generated that relies on the hybrid particle Petri nets (HPPN) data structure. The prognosis process uses the current diagnosis of the system to predict its future states and to determine its end of life (EOL) or its remaining useful life (RUL). The HPPN-based prognoser is initialized with a stochastic scaling algorithm that selects the diagnosis hypotheses with the highest beliefs. The SSA provides a compromise between performance and available computational resources through the setting of scaling parameters. The prognoser then uses the future commands to determine the hypotheses over the rover future trajectory and the RUL/EOL. The set of future hypotheses associated with their belief degrees forms the current rover prognosis. The prognosis method is tested on different scenarios, with different scaling parameters, considering whether the future commands are known or not. Experimental results show that the approach is robust to real system data and computational performance constraints.