Research on Diagnosis Data Fusion of Aero-engine based on Improved K-means Cluster and D-S Evidence Theory

DOI

10.2991/iiicec-15.2015.147How to use a DOI?

Keywords

aero-engine; D-S evidence theory; data fusion

Abstract

The data fusion method combined improved K-means clustering algorithm with D-S evidence theory was used for vibration fault data fusion of aero-engine in this paper. The later calculated amount was reduced by the improved K-means clustering algorithm. On the basis of the improved K-means clustering algorithm, the basic belief function of vibration data was determined. D-S evidence theory was used for fusion of fault vibration data of aero-engine which had been processed through the improved K-means clustering analysis. The results of diagnostic instance show that the method can improve the diagnosis rates of aero-engine fault effectively.

Copyright

© 2015, 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  - Mingming Zhang
AU  - Xiaobo Liu
PY  - 2015/03
DA  - 2015/03
TI  - Research on Diagnosis Data Fusion of Aero-engine based on Improved K-means Cluster and D-S Evidence Theory
BT  - Proceedings of the 2015 International Industrial Informatics and Computer Engineering Conference
PB  - Atlantis Press
SP  - 646
EP  - 650
SN  - 2352-538X
UR  - https://doi.org/10.2991/iiicec-15.2015.147
DO  - 10.2991/iiicec-15.2015.147
ID  - Zhang2015/03
ER  -