A supervisory system for space missions is critical due to the high risk of missions, the costs, and the impossibility of adding redundancy. The model-based fault detection approaches are of interest due to their highly responsive speed, robustness against disturbances and uncertainties, and accuracy. Conventional model-based methods have some drawbacks such as feasibility and applicability. In this paper, a modified extended multiple models adaptive estimation (MEMMAE) method is developed which keep both the advantages of the previous model-based methods and take into account some limitations of that. This approach can be performed on various systems to detect and diagnose faults, with appropriate response speed and resistance to uncertainty and disturbances. By combining the recursive least-square algorithm with the extended multiple model adaptive estimation (EMMAE) method, the limitations of this method including simultaneous fault detection, diagnostics of failure cause, and high processing volume are eliminated. The method is implemented on a spacecraft as a case study using the MATLAB/SIMULINK software and demonstrates that the responsive speed and accuracy of the proposed method is significantly much more effective and accurate than the previous method.