Performance Degradation Assessment of Rolling Bearing Based on both ADMM and Sparse Combination Learning
Fei Luo, Yuhua Zhou
Available Online July 2016.
- https://doi.org/10.2991/iccia-17.2017.107How to use a DOI?
- rolling bearing performance degradation assessment, Convolutional Sparse Combination Learning, ADMM algorithm, IMS.
- In order to solve the problem of evaluating performance degradation of rolling bearing, this paper proposes a model containing both Alternating Direction Method of Multipliers (ADMM) and Convolutional Sparse Combination Learning. We first introduce the model during learning phase and test phase. Finally, we do an experiment on a database provided by the Center for Intelligent Maintenance Systems (IMS), University of Cincinnati. From experimental results, the proposed model is feasible.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Fei Luo AU - Yuhua Zhou PY - 2016/07 DA - 2016/07 TI - Performance Degradation Assessment of Rolling Bearing Based on both ADMM and Sparse Combination Learning BT - Proceedings of the 2nd International Conference on Computer Engineering, Information Science & Application Technology (ICCIA 2017) PB - Atlantis Press SP - 630 EP - 634 SN - 2352-538X UR - https://doi.org/10.2991/iccia-17.2017.107 DO - https://doi.org/10.2991/iccia-17.2017.107 ID - Luo2016/07 ER -