Missing Data Rolling Bearing Remaining Useful Life Prediction Fusion Auto-Correlation and Cross-Correlation
Author:
Affiliation:
1. Shanghai Maritime University,School of Logistic Engineering,Shanghai,China
Funder
National Natural Science Foundation of China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10295546/10295577/10295693.pdf?arnumber=10295693
Reference21 articles.
1. Imputation of Missing Data in Time Series for Air Pollutants Using Long Short-Term Memory Recurrent Neural Networks
2. Smoothed LSTM-AE: A spatio-temporal deep model for multiple time-series missing imputation
3. Brits: Bidirectional recurrent imputation for time series[J];cao;Advances in neural information processing systems,2018
4. Temporal Belief Memory: Imputing Missing Data during RNN Training
5. LSTM-Based Multi-Task Method for Remaining Useful Life Prediction under Corrupted Sensor Data
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