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Utilizing Autoregressive Networks for Full Lifecycle Data Generation of Rolling Bearings for RUL Prediction
Our findings demonstrate that the CVGAN model, in terms of both MMD and FID metrics, outperforms many advanced methods in both autoregressive and non-autoregressive generation modes.
Utilizing Multiple Inputs Autoregressive Models for Bearing Remaining Useful Life Prediction
Accurate prediction of the Remaining Useful Life (RUL) of rolling bearings is crucial in industrial production, yet existing models often struggle with limited generalization capabilities due to their inability to fully process all vibration signal patterns.
Utilizing VQ-VAE for End-to-End Health Indicator Generation in Predicting Rolling Bearing RUL
The prediction of the remaining useful life (RUL) of rolling bearings is a pivotal issue in industrial production.
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