Fault Diagnosis Method for Heterogeneous Information Fusion of Permanent Magnet Generator Considering Classifier Performance and Weight of Evidence

DOI

10.2991/icsd-16.2017.118How to use a DOI?

Keywords

Permanent magnet synchronous generator; Support vector machine; Mechanical and electrical integrated information; Weight fusion; Potential fault diagnosis

Abstract

Aiming at the problem that the potential fault of the permanent magnet synchronous generator is difficult to be accurately identified, a potential fault diagnosis model based on probability output of multi-class support vector machine (SVM) and improved D-S evidence theory is proposed. Furthermore, the generator stator current and vibration characteristics are applied in the establishment of diagnostic model respectively and the failure probability based on the heterogeneous feature is obtained. Considering the difference of the fault characterization ability between the current evidence and the vibration evidence, as well as the generalization ability of SVM, the weight fusion model is established, and the output of the model is the final diagnosis criterion.

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  - Xun Yin
AU  - Xin-Yan Zhang
AU  - Shao-Ran Wang
AU  - Lu-Lu Yang
AU  - Zhi-Wen Luo
AU  - Li-Wei Zhao
PY  - 2016/12
DA  - 2016/12
TI  - Fault Diagnosis Method for Heterogeneous Information Fusion of Permanent Magnet Generator Considering Classifier Performance and Weight of Evidence
BT  - Proceedings of the 2nd 2016 International Conference on Sustainable Development (ICSD 2016)
PB  - Atlantis Press
SP  - 542
EP  - 545
SN  - 2352-5401
UR  - https://doi.org/10.2991/icsd-16.2017.118
DO  - 10.2991/icsd-16.2017.118
ID  - Yin2016/12
ER  -