Abstract
Aiming at the possible mechanical faults of UAV rotor in the working process, this paper proposes a UAV rotor fault identification 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 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 effect of minor faults is not ideal, which causes by all states were identified as normal and then reduces the overall identification accuracy, when using conventional sequential sampling to construct learning. 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 vibration signal is proposed. And it is also verified that the sample set reconstructed can easily reflect the global information of mechanical operation. Finally, according to the comparison of analysis results, the recognition rate of 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 combing the proposed method of interval sampling reconstruction.
A New Deep Learning Model for Fault Diagnosis with Good Anti-Noise and Domain Adaptation Ability on Raw Vibration Signals
