Remaining Useful Life Prediction of Crane Rolling Bearings Based on RMS Trend Consistency
As a key component of crane equipment rotating mechanism,the health status of rolling bearings will directly affect the safety of crane equipment.Due to the complexity of crane equipment working conditions,the degradation trend of bearings of the same type is inconsistent,which makes the training parameters of the prediction model training set and the test set unsuitable,and reduces the prediction accuracy of the model.Aiming at the above problems,a signal reconstruction method is proposed,in which the root mean square(RMS)of the vibration signal is reconstructed to effectively improve the trend consistency,and is inputted into the remaining useful life(RUL)prediction model GRUA for prediction.The XJTU-SY dataset is used to validate the method,and the results show that the reconstructed RMS is used as the input to GRUA to effectively improve the prediction accuracy of the model.
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维普
