Assessment and prediction of railway station equipment health status based on graph neural network

HIGHLIGHTS

SUMMARY

In this method, a recurrent neural_network based on encoder-decoder framework with attention mechanism was proposed to predict Health Indicate (HI) values that are closely related to RUL values. Ren et_al transformed feature values into feature maps as the input of deep convolutional neural_networks to effectively predict the RUL of bearings. Zhang et al used a Long Short-Term MemoryRecurrent Neural_Network (LSTM-RNN) to synthesize a datadriven RUL predictor, and the constructed LSTM-RNN can be used for offline data training to predict the battery RUL. Costa et_al proposed a deep learning method for . . .

 



If you want to have access to all the content you need to log in!

Thanks :)

Username or Email

Password

Remember me

Lost your password?

If you don't have an account, you can create one here.