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The oxygen generation system by using electrolytic water in space station is a key physical and chemical recycling equipment. Its operating state directly determines the activation frequency of the standby oxygen cylinder and endangers the life safety of the astronauts. For high-precision control of oxygen production and system fault diagnosis, we need to obtain high-accuracy estimation of the inner state of the system, e.g. partial oxygen pressure in the electrolysis unit. In this paper, a novel state estimation algorithm named stochastic projection Kalman filter is proposed. Compared with EKF and UKF algorithm, the new algorithm can obtain higher estimation precision and better convergence.
Inspec keywords: Kalman filters; stochastic processes; fault diagnosis; oxygen; aerospace safety; electrolysis; recycling; state estimation; space vehicles
Subjects: Plant engineering, maintenance and safety; Other topics in statistics; Filtering methods in signal processing; Other topics in aerospace