Prediction Method for Bearing Remaining Useful Life Based on Convolutional Neural Networks
To improve the prediction accuracy and generalization ability of the Remaining Useful Life(RUL)prediction model for escalator bearings,a bearing RUL prediction method based on Convolutional Neural Network(CNN)is proposed.Firstly,it denoises the original data based on 3σ criterion,obtains its frequency characteristics through fast Fourier transformation.Secondly,it applies layered sampling different from traditional time series data partitioning methods to data partitioning,and constructs a Deep Convolutional Neural Network(DCNN)model consisting of three convolutional layers and two fully connected layers.Finally,the NASA IMS dataset is used to evaluate the preprocessing method,DCNN model accuracy,and generalization ability,proving the superiority of this method.
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