Performance Degradation Assessment of Rolling Bearing Based on both ADMM and Sparse Combination Learning

DOI

10.2991/iccia-17.2017.107How to use a DOI?

Keywords

rolling bearing performance degradation assessment, Convolutional Sparse Combination Learning, ADMM algorithm, IMS.

Abstract

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.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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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  - 10.2991/iccia-17.2017.107
ID  - Luo2016/07
ER  -