| Issue |
E3S Web Conf.
Volume 118, 2019
2019 4th International Conference on Advances in Energy and Environment Research (ICAEER 2019)
|
|
|---|---|---|
| Article Number | 02036 | |
| Number of page(s) | 5 | |
| Section | Energy Equipment and Application | |
| DOI | https://doi.org/10.1051/e3sconf/201911802036 | |
| Published online | 04 October 2019 | |
Research on Fault Diagnosis Model of Rotating Machinery Vibration Based on Information Entropy and Improved SVM
1
ENGINEER, Huadian Electric Power Research Institute, NO. 2Xiyuan 9 Road, Hangzhou Zhejiang Province, 310030, China
2
SENIOR ENGINEER, Huadian Electric Power Research Institute, NO. 2 Xiyuan 9 Road, Hangzhou Zhejiang Province, 310030, China
3
SENIOR ENGINEER, Huadian Electric Power Research Institute, NO. 2 Xiyuan 9 Road, Hangzhou Zhejiang Province, 310030, China
4
ENGINEER, Huadian Electric Power Research Institute, NO. 2 Xiyuan 9 Road, Hangzhou Zhejiang Province, 310030, China
* Corresponding author: Hankun Bing: bing_hankun@163.com
Based on the concept of information entropy, this paper analyzes typical nonlinear vibration fault signals of steam turbine based on spectrum, wavelet and HHT theory methods, and extracts wavelet energy spectrum entropy, IMF energy spectrum entropy, time domain singular value entropy and frequency domain power spectrum entropy as faults. The feature is supported by a support vector machine (SVM) as a learning platform. The research results show that the fusion information entropy describes the vibration fault more comprehensively, and the support vector machine fault diagnosis model can achieve higher diagnostic accuracy.
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.