Unmanned aerial vehicle rotor fault diagnosis based on interval sampling reconstruction of vibration signals and a one-dimensional convolutional neural network deep learning method

Abstract With the aim of identifying possible mechanical faults in unmanned aerial vehicle (UAV) rotors during operation, this paper proposes a method based on interval sampling reconstruction of vibration signals and one-dimensional convolutional neural network (1D-CNN) deep learning. Firstly, experiments were designed to collect the vibration acceleration signals of a UAV working at high speed under three states (normal, rotor damage by varying degrees, and rotor crack by different degrees). Then, considering the powerful feature extraction and complex data analysis abilities of 1D-CNN, an effective deep learning model for fault identification is established utilizing 1D-CNN. During analysis, it is found that the recognition rate for minor faults is not ideal, with all weak states being identified as normal, reducing the overall identification accuracy, when using conventional sequential sampling to construct learning sample sets. To this end, in order to make the sample data cover the whole process of data collection as much as possible, a learning sample processing method based on interval sampling reconstruction of the vibration signal is proposed. And it is also verified that the reconstructed sample set can easily reflect the global information of mechanical operation. Finally, according to the comparison of analysis results, the recognition rate of the deep learning model for different degrees of faults is greatly improved, and minor faults could also be accurately identified through this method. The results show that the 1D-CNN deep learning model could diagnose and identify UAV rotor damage faults accurately, by incorporating the proposed method of interval sampling reconstruction.