基于SDAE和GRUNN的行星齿轮故障识别

In order to address the problem of low fault identification accuracy of planetary gears under noisy environment and time-varying rotational speed conditions, a fault diagnosis method for planetary gears using the stacked denoising autoencoder (SDAE) and gated recurrent unit neural network (GRUNN) was proposed. A hybrid model based on the SDAE and GRUNN was constructed to process pre and post correlation time-series data, and automatically extract robust fault features. The training samples for planetary gear fault diagnosis were regarded as the input data of the hybrid model. The Adam optimization algorithm and the dropout technique were employed to train the hybrid model so as to realize the optimization of multiple parameters and prevent from overfitting. A softmax classifier was employed to identify the planetary gear states of test samples according to the hybrid model after training. The effectiveness of the proposed method was validated through a fault identification experiment of planetary gears. The experimental results demonstrate that the proposed method is of stronger anti-noise ability and excellent adaptability to time-varying rotational speed.