Fan fault diagnosis based on symmetrized dot pattern and improved BP neural network

DOI

10.2991/icmemtc-16.2016.176How to use a DOI?

Keywords

Centrifugal fan; Fault diagnosis; Symmetrized dot pattern (SDP); Improved Back Propagation (BP) neural network

Abstract

To accurately diagnose the mechanical failure of fan, the method based on the symmetrized dot pattern (SDP) analysis and improved Back Propagation (BP) neural network is proposed. Vibration signals acquisition of 13 kinds of running states were achieved on the 4-73 No.8D centrifugal fan test rig and the SDP technique was utilized to reconstruct the vibration signals. Then, the features of the SDP pattern of each running state were extracted and the fault eigenvectors based on the multiple feature fusion were constructed. Finally, the sample set of the eigenvectors was trained and tested by the improved BP neural network to diagnose the mechanical failure of the fan. The results show that the fan fault diagnosis method based on the SDP analysis and improved BP neural network can effectively diagnose the category, severity and site of the fan mechanical failures with high diagnosis rate, short testing time and good online diagnosis performance.

Copyright

© 2016, 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  - Songling Wang
AU  - Haixiao Liu
AU  - Xiaogang Xu
PY  - 2016/04
DA  - 2016/04
TI  - Fan fault diagnosis based on symmetrized dot pattern and improved BP neural network
BT  - Proceedings of the 2016 3rd International Conference on Materials Engineering, Manufacturing Technology and Control
PB  - Atlantis Press
SP  - 899
EP  - 902
SN  - 2352-5401
UR  - https://doi.org/10.2991/icmemtc-16.2016.176
DO  - 10.2991/icmemtc-16.2016.176
ID  - Wang2016/04
ER  -