To solve the problem of inaccurate remaining useful life prediction caused by the imbalance of bearing monitoring data,this paper proposes a RUL prediction method based on the generation of long-short term memory network in the confrontation domain.At first,the vibration signals of the original data are extracted to construct the training dataset and test dataset.Next,a long-short term memory network is used to replace the generator module in the Wasserstein generative adversarial network to construct a new Wasserstein generative adversarial network framework and augment the original data to create a new equilibrium dataset.Finally,the dataset is fed into the long-short term memory network for remaining useful life prediction and the performance of the proposed method is evaluated by the bearing degradation dataset and the aero-engine dataset disclosed in PHM2012.The experimental results show that Wasserstein can improve the data imbalance state by generating the confrontation network.Using the balanced data set to train the long-short term memory network can effectively improve the prediction ability of the performance degradation trend and improve the prediction accuracy.
关键词
剩余寿命预测/长短期记忆网络/Wasserstein生成对抗网络
Key words
Remaining Useful Life Prediction/Long-short Term Memory Network/Wasserstein Generative Adversarial Network
维普
万方数据