Fault Diagnosis of Aircraft Landing Gear Retractable System Based on BO-SVM

In response to the problems of poor recognition accuracy and low speed in traditional machine learning algorithms for fault diagnosis of aircraft landing gear retraction and retraction systems, a fault diagnosis method for landing gear retraction and retraction systems is proposed on the basis of the analysis of the landing gear retraction and retraction system model, which combines Bayesian optimization with support vector machines. By analyzing the structure and function of the landing gear retraction and retraction system, an AMESim model of the landing gear retraction and retraction system is established to obtain fault simulation data; the key feature quantity is extracted from the original data by combining the time-domain feature and frequency-domain feature, and normalized to obtain the standard model input data; Relying on Bayesian optimization's efficient, flexible, and precise optimization ability to find the optimal penalty factor and kernel function of support vector machines; Through simulation experiments and comparative analysis with various faults diagnosis algorithms, the results show that this algorithm has high accuracy and fast diagnostic speed, verifying the superiority of BO-SVM. It can achieve fault diagnosis for landing gear retraction and retraction systems and has maybe of value to the engineering.