Performance Degradation Prediciton of Rolling Bearings based on BP Neural Networks

DOI

10.2991/icmia-17.2017.133How to use a DOI?

Keywords

Evaluation of bearing performance degradationImage fusion; time-frequency analysis; BP neural network

Abstract

Roller bearings are commonly used components in rotating machinery and are pruned to be failure, which may cause the system break down and result in economic loss. Therefore, performance degradation prediction of rolling bearings is important to prevent any unexpected roller bearings failure. In this paper, time - frequency image fusion technology as well as the BP neural networks are utilized for fault feature extraction and prediction based on vibration signals.BP neural networks are used to learn the fault prediction features of vibration signals. Finally, the test data is used to testing the whole neural networks to establish the bearing condition monitoring model. Experimental results show that the proposed method achieves is about 80% accuracy to the bearing state recognition, and the recognition rate of the degraded performance stage is the highest, which can meet the engineering requirements.

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  - Xiaoxuan Qi
AU  - Size Wang
AU  - Jingfeng Liu
PY  - 2017/06
DA  - 2017/06
TI  - Performance Degradation Prediciton of Rolling Bearings based on BP Neural Networks
BT  - Proceedings of the 2017 6th International Conference on Measurement, Instrumentation and Automation (ICMIA 2017)
PB  - Atlantis Press
SP  - 740
EP  - 743
SN  - 1951-6851
UR  - https://doi.org/10.2991/icmia-17.2017.133
DO  - 10.2991/icmia-17.2017.133
ID  - Qi2017/06
ER  -