Abstract:

Rolling bearing is one of the key components in rotating machinery, and long-term mechanical operation leads to wear easily. Traditional fault diagnosis relies on feature extraction, but due to loud noise during mechanical operation, effective signals are drowned. And the fault diagnosis network structure is complicated and there are too many parameters. Therefore, a bearing fault diagnosis model based on variational mode decomposition and deep learning is proposed for bearing wear detection. Firstly, the bearing signal is decomposed by VMD( Variational Mode Decomposition) and denoised by Hausdorff distance. Secondly, the

selected effective signals are inputted into the network structure of one-dimensional convolutional neural network and gate recurrent unit to complete the classification of data and realize the fault diagnosis of bearings. Compared to common bearing fault diagnosis methods, the proposed VMD-1DCNN-GRU(Variational Mode Decomposition- 1D Convolutional Neural Networks-Gate Recurrent Unit) model has the highest accuracy. The experimental results verify the feasibility of the proposed model for the effective classification of bearing faults, which has certain research significance.

Key words: fault diagnosis, deep learning, variational mode decomposition ( VMD), convolutional neural network(CNN), gate recurrent unit(GRU)