Fault detection of least squares support vector machine flight control system based on sparrow algorithm

The operational status fault detection of the flight control system plays a crucial role in the safe and smooth flight of the overall system. In this paper, a data-driven approach is used to detect several typical faults of flight control system sensors, and a least-squares support vector machine flight control system sensor fault detection method based on the sparrow algorithm is proposed. To address the problem of blind parameter selection in the least squares support vector machine model (LSSVM), the sparrow algorithm is used for parameter search. This paper proposes a sensor fault detection model for flight control system based on KPCA-SSA-LSSVM, and the sparrow algorithm is used to perform parameter search optimization on LSSVM to enhance the fault diagnosis performance of the algorithm. Experiments prove that the diagnosis accuracy of the algorithm reaches 98.23%, much higher than other fault diagnosis algorithms, which proves the rationality of the proposed algorithm in this paper.