A Domain Adaptive Method with Orthogonal Constraint for Predicting the Remaining Useful Life of Rolling Bearings under Cross Working Conditions
To address the problems that blurred decision boundaries and low identifiability of features in the rolling bearing Remaining Useful Life (RUL) prediction under cross working conditions, a domain adaptive method with Maximum Classifier Discrepancy network with Orthogonal Constraints (MCD_OC) is proposed. Firstly, the fast Fourier transform is applied to transform the raw vibration signal into the frequency domain signal and input it to the model. Then, Convolutional Neural Network (CNN) and Gate Recurrent Unit (GRU) are used to extract the depth spatiotemporal features of the bearing signal, the source and target domain feature is aligned using the maximum classifier discrepancy, and the orthogonal constraint is applied to constrain target domain features to increase the identifiability between features of unlabeled target domain feature. Finally, comparative experiments are conducted on the prediction of cross working condition RUL predict based on the bearing life dataset to evaluate the method in this work, and the optimal results are obtained in multiple experiments.
Rolling bearingRemaining Useful Life(RUL)Orthogonal constraintMaximum classifier discrepancy
万方数据
维普
