Fault diagnosis method of multi-rotor UAV based on one-dimensional convolutional neural network with adaptive batch normalization algorithm

Abstract

In this paper, we propose a one-dimensional convolutional neural network(1D-CNN) model based on the adaptive batch normalization (AdaBN) algorithm to improve the CNN model, which is difficult to extract features from multi-rotor unmanned aerial vehicle (UAV) rotor structural faults under variable conditions and has poor fault diagnosis performance. The method accomplishes fault diagnosis and classification by feature extraction from lower dimensional multi-rotor UAV data. The AdaBN algorithm adjusts the parameters of the BN layer in the model during the testing phase to improve the domain adaptive capability of the model in scenarios with variable operating conditions. Also, to improve the robustness of the model under noisy conditions, the first layer of convolutional kernel dropout operation is introduced to improve the noise immunity of the model. To reduce the complexity of manual tuning and to find the optimal combination of hyperparameters for the network model more effectively, the grey wolf optimizer algorithm is used to optimize the hyperparameters and further improve the model performance. Finally, the effectiveness of the proposed method is verified through comparison tests, and it shows good diagnostic effects in noise and variable conditions compared with several commonly used fault diagnosis methods.

Unmanned aerial vehicle fault diagnosis based on ensemble deep learning model

“…Du et al [25] achieved a high recognition rate of rotor faults of UAV through the powerful feature extraction capability of CNN and accurately identified smaller faults by interval sampling reconstruction. Yang et al [26] improved the CNN model through the adaptive batch normalization algorithm (AdaBN) to improve the extraction problem of structural fault features of multi-rotor UAV under variable operating conditions. Recurrent neural network such as LSTM and GRU are being widely used in fault diagnosis due to their good time series data processing capability.…”