Nonlinear predictive filter based fault diagnosis of oxygen generation system by using electrolytic water in space station

The oxygen generation system using electrolytic water is one of the important systems in space station which supplies the oxygen for the astronauts from the condensate water, i.e., Sabatier reduction reaction water and urine treated water. During the operation of the space station, the normal operation of the system directly affects the safety of astronauts and the operating costs of the space station, so the fault diagnosis of the system has become a research hotspot. This paper systematically describes the fault diagnosis method of the oxygen generation system using electrolytic water, including the establishment of the fault mode and effects analysis, mathematical model of the key components and the estimation algorithm of unmeasured parameters. For the two direct discriminant indicators of the oxygen production and hydrogen production, because they are unmeasurable and their dynamic models are difficult to be established, this paper proposes a cascade algorithm of nonlinear predictive filtering and low-pass filter to solve the problem. Firstly, the oxygen and hydrogen production are estimated by the predictive filter. And then, the high frequency noise introduced by measurement error is eliminated when the oxygen and hydrogen production signals entered in a low-pass filter. Through the simulation analysis of the normal and fault mode, the correctness and effectiveness of the estimation algorithm are illustrated.