Bearing fault diagnosis based on particle swarm optimization fusion convolutional neural network - Liu, Xian; Wu, Ruiqi; Wang, Rugang; Zhou, Feng; Chen, Zhaofeng; Guo, Naihong (Frontiers in Neurorobotics, 2022)
Aiming at the low quality of feature extraction and the decrease in recognition accuracy under different working conditions in traditional convolutional neural network bearing fault diagnosis, a bearing fault model based on particle swarm optimization and convolutional neural network was designed. The model first adaptively adjusts the hyperparameters of the model through PSO particle swarm optimization, then introduces residual connections to prevent the gradient from disappearing, uses global average pooling to replace the fully connected layer to reduce the training parameters of the model, finally adds a dropout layer to prevent Network overfitting. The experimental results show that the model under 4 conditions, two of which can achieve 100% recognition, and the other two can also achieve more than 98% accuracy. And compared with the traditional diagnosis method, the model has higher accuracy under variable working conditions.