Soft decision-making based on decision-theoretic rough set and Takagi-Sugeno fuzzy model with application to the autonomous fault diagnosis of satellite power system

The satellite power system is one of the core systems to ensure the normal on-orbit operation of satellite, and is also a representative of typical complex nonlinear systems. Autonomous prognostics and health management (A-PHM) is an inevitable trend in the future development of satellite, and autonomous fault diagnosis is a key part of A-PHM. Therefore, it is very necessary to carry out the research on autonomous fault diagnosis for satellite power system to improve the capability of satellite to perform its on-orbit tasks independently. Therefore, we put forward a feasible framework of soft decision-making to cope with this issue, mainly including attribute reduction, attribute weight assignment, rule extraction, and rule matching. Specifically, a neighborhood decision-theoretic rough set model (named DNDTRS) is first designed with the help of a data-driven loss function matrix for attribute reduction and weight assignment. Subsequently, the classification probability generated by DNDTRS is fed to a rule extraction model developed by the Takagi-Sugeno (T-S) fuzzy theory. Finally, a soft decision-making mechanism is proposed to execute the output after rule matching. In the experimental part, the proposed methodology is verified by the benchmark datasets and the fault data of satellite power system. The experimental results demonstrate the promised performance of our methodology.