A Robust Bearing Remaining Useful Life Prediction Method based on Degradation IMFs Selection

Author:

Cao Biaobiao1ORCID,Jiang Li1ORCID

Affiliation:

1. School of Mechanical and Electronic Engineering, Wuhan University of Technology, hubei, wuhan, china, China

Publisher

ACM

Reference10 articles.

1. Zhu J, Chen N, Peng W. Estimation of Bearing Remaining Useful Life Based on Multiscale Convolutional Neural Network. IEEE Transactions on Industrial Electronics 2019; 66:3208-3216.

2. Polygon-based large diameter measurement with modular gauges

3. Cubillo A Perinpanayagam S Esperon-Miguez M. A review of physics-based models in prognostics: Application to gears and bearings of rotating machinery. Advances in Mechanical Engineering 2016; 8.

4. Zhang Y, Xiong R, He H, Pecht MG. Long Short-Term Memory Recurrent Neural Network for Remaining Useful Life Prediction of Lithium-Ion Batteries. IEEE Transactions on Vehicular Technology 2018; 67:5695-5705.

5. Liu K, Shang Y, Ouyang Q, Widanage WD. A Data-Driven Approach With Uncertainty Quantification for Predicting Future Capacities and Remaining Useful Life of Lithium-ion Battery. IEEE Transactions on Industrial Electronics 2021; 68:3170-3180.

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