Aiming at rolling bearing vibration signals under complex working conditions, a bearing fault diagnosis classification method based on the generalized regression neural network-SOFTMAX (GRNN-SOFTMAX) classification model were proposed to realize bearing fault mode identification.Firstly, the variational mode decomposition (VMD) was performed for rolling bearing vibration signals to do feature extraction and other pre-processing, and obtain a feature data set.The feature data set was divided into a training one, a verification one and a test one.Then, the training set and test set were used to train the GRNN-SOFTMAX classification model.The grey wolf optimizer (GWO) was introduced to optimize the key parameter’s smoothing factor of the above model, and obtain an ideal classification model.Finally, the trained model was applied in the test set to output the fault identification results.Through simulation tests, bearing fault data under different working conditions was collected to verify the effectiveness of the proposed method.Results showed that the proposed method can use a small sample training set to realize effective diagnosis of bearing faults under different working conditions; it is a fault diagnosis method suitable for actual working conditions.
Key words
fault diagnosis /
rolling bearing /
general regression neural network (GRNN) /
SOFTMAX normalization /
grey wolf optimizer (GWO)
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