Study on blade fault diagnosis of free flying UAV based on multiparameter and deep learning method

Abstract

With the widespread application of unmanned aerial vehicles (UAVs) in various fields, more and more attention has been paid to the operation status monitoring and fault diagnosis of UAVs. During the use of drones, the motors, blades, connectors and other components may inevitably experience wear, fatigue, and breakage, which are difficult to directly monitor through sensors. Therefore, a fault identification method based on one-dimensional convolutional neural network (1D-CNN) is proposed to provide ideas for the research on the mechanical fault diagnosis of UAVs. A Bluetooth wireless acceleration-attitude sensor is used to collect the acceleration, angular velocity and angle of the free flying UAV in X, Y, and Z directions. With these characteristic parameters as sample data, fault identification can be implemented using deep learning model. Besides, to deal with over-fitting, a data reconstruction method of partition sampling is proposed. By comparing different input parameters and optimization functions, we found that when using multi-parameter + RMSProp optimization, the recognition accuracy reaches 98%. In addition, a comparative analysis is carried out using shallow neural network PNN and SVM methods, and the results show that the proposed 1D-CNN model outperforms both shallow neural networks and traditional machine learning methods.