Proceedings of the 2017 5th International Conference on Frontiers of Manufacturing Science and Measuring Technology (FMSMT 2017)

Research on fault diagnosis of indicator diagram based on BP neural network optimized by iterative learning control

Authors
Xiaohong Hao, Ning Zhang
Corresponding Author
Xiaohong Hao
Available Online April 2017.
DOI
https://doi.org/10.2991/fmsmt-17.2017.169How to use a DOI?
Keywords
pumping unit,fault diagnosis,indicator diagram,Shape invariant moment,Fourier descriptor,Iterative learning control, BP neural network.
Abstract

In this paper, an intelligent fault diagnosis method is presented to solve the problem that the accuracy of pumping unit fault diagnosis is not high. This method selects the Shape invariant moment and the Fourier descriptor to extract the characteristic parameters of the sample indicator diagram, and uses the BP neural network optimized by iterative learning control to classify and identify. Finally, the expert diagnosis method is used to compare the recognition results to obtain the diagnosis result. The experiment shows that the method is fast, accurate and intelligent.

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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Volume Title
Proceedings of the 2017 5th International Conference on Frontiers of Manufacturing Science and Measuring Technology (FMSMT 2017)
Series
Advances in Engineering Research
Publication Date
April 2017
ISBN
978-94-6252-331-9
ISSN
2352-5401
DOI
https://doi.org/10.2991/fmsmt-17.2017.169How to use a DOI?
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/).

Cite this article

TY  - CONF
AU  - Xiaohong Hao
AU  - Ning Zhang
PY  - 2017/04
DA  - 2017/04
TI  - Research on fault diagnosis of indicator diagram based on BP neural network optimized by iterative learning control
BT  - Proceedings of the 2017 5th International Conference on Frontiers of Manufacturing Science and Measuring Technology (FMSMT 2017)
PB  - Atlantis Press
SP  - 881
EP  - 889
SN  - 2352-5401
UR  - https://doi.org/10.2991/fmsmt-17.2017.169
DO  - https://doi.org/10.2991/fmsmt-17.2017.169
ID  - Hao2017/04
ER  -