Abstract: An insulator fault detection method based on ensemble convolution wavelet extreme learning neural network is proposed because the traditional methods can not accurately and efficiently identify the faults of insulators due to the remote distribution position and complex background. Firstly, the insulator images data is collected and preprocessed by industrial camera installed on UAV (Unmanned Aerial Vehicle). Secondly, the ensemble convolution wavelet extreme learning neural network is constructed by combining the advantages of convolution neural network, auto encoder, extreme learning machine and wavelet function. Finally, the insulator images samples are fed into multiple deep neural networks for automatic feature learning. The prediction results are assembled and the final fault detection results are output. The experimental results show that the average fault detection accuracy of the proposed method reaches 98. 49% and the standard deviation is only 0. 20. Compared to other methods, it has more advantages in image feature extraction and fault detection accuracy, and is suitable for automatic identification of insulator faults.

Key words: insulator, extreme learning machine, neural network, fault detection, convolution neural network; ensemble learning