Fault Diagnosis and Recovery of UAV Based on FCM-(PSO-DF) Algorithm

In order to realize UAV fault detection and recovery in complex flight modes, a data-driven FCM-(PSO-DF) fusion algorithm is proposed which consists of fuzzy c-means (FCM), particle swarm optimization (PSO) and deep forest (DF) algorithm. Firstly, the flight data is preprocessed to remove abnormal values and high-frequency noise interference. Then, FCM clustering algorithm is used to divide the flight data into four clusters of samples according to different flight modes. Secondly, PSO-DF is designed as the regression model, and four groups of clustering samples are trained respectively to obtain the estimation of monitoring parameters. Then, the residual error between the actual flight data and the estimated value is calculated, the mean value of residuals is set as statistical threshold, and the residual error is compared with the statistical threshold to realize fault detection. Finally, the fault data is replaced with estimates to achieve fault recovery. In order to verify the effectiveness of this method, experiments are carried out using real flight data, and set up the comparison experiments with other algorithms. Experimental results show that the algorithm is effective on the failure detection and recovery of UAV flight data.