Kinetic-motion dissimilar monitoring based inertial data fault detection for flight control system safety enhancement

There are three major challenges in inertial data redundancy management for flight control systems: common-mode risk suppression, correct voting on singular faults, and single data available. This paper proposes a fault detection algorithm and a redundancy management architecture based on motion dissimilar monitoring. The core of this architecture lies in establishing the relationships among attitude, angular rate, load factor, and body-axis acceleration through engineering-oriented soft-reconfiguration calculations based on simplified motion models. This enables effective fault detection in inertial data and enhances the safety of the flight control system. Real flight test data is used to construct a ground-based offline flight test verification platform, and flight data gathered under boundary flight conditions is used to evaluate the proposed algorithm. The results show that under normal inertial data conditions, the body-axis overload calculated theoretically based on the aircraft dynamics is highly consistent with the actual measured overload, with a maximum deviation of less than 0.1g. However, there is a noticeable difference between the measured and theoretically computed body-axis overload when any of the angular rate, overload, or attitude signals in the inertial data are faulty. This allows for precise fault identification and isolation without the addition of new hardware, effectively enhancing the safety of the flight control system.