An Abnormal Detection Method of Rocket Power System Based on Improved Support Vector Machine

Aiming at the problem of anomaly monitoring due to the insufficient sensors and low confidence level of the rocket power system, an improved support vector machine based anomaly monitoring method for the rocket power system is proposed. Firstly, a closed loop of the rocket control system is built, and the flight state dataset is constructed by selecting suitable measurable parameters. Secondly, the LSTM Auto encoder algorithm is used to reconstruct the flight state data to obtain the residual data. Then, the support vector machine model is constructed, and the artificial bee colony algorithm is used to find the optimal classification parameters for the support vector machine parameters. The residual dataset is input to the support vector machine model. Finally, the effectiveness and feasibility of the algorithm are verified via the closed loop simulation data.