Active Mission Success Estimation through PHM-Informed Probabilistic Modelling

Abstract


 
 
 Prognostics and Health Management (PHM) techniques have traditionally been used to analyze electrical and mechanical systems, but similar techniques can be adapted for less mechatronically-focused processes such as crewed space missions. By applying failure analysis techniques taken from PHM, the probability of success for missions can be calculated. Extensive work has been conducted to predict space mission failure, but many existing methods do not take full advantage of modern computing power and the potential for real-time calculation of mission failure probabilities. The Active Mission Success Estimation (AMSE) method is developed in this paper to track and calculate the probability of mission failure as the mission progresses, and is intentionally adaptable for shifting mission objectives and parameters. This form of mission modelling takes a broader view of the mission and objectives, and develops statistical probability models of success or failure for multiple possible choice combinations that is used to inform real-time decisions and maximize probability of mission success. A case study of a generalized crewed Mars mission that has turned into a survival scenario is considered where an astronaut has been left behind on the surface and must survive for an extended period of time before undertaking a long-distance journey to a new launch site for rescue and return to Earth. The AMSE method presented here aims to establish real-time probabilistic modeling of decision outcomes during an active mission and can be used to inform mission decisions.