Proceedings of the 2016 4th International Conference on Advanced Materials and Information Technology Processing (AMITP 2016)

Study Of Vertical Emulsifier Fault Diagnosis System Based On EMD-Sample Entropy And BP Neural Network

Authors
Yue-sheng Wang, Yao-wen Sun
Corresponding Author
Yue-sheng Wang
Available Online September 2016.
DOI
https://doi.org/10.2991/amitp-16.2016.103How to use a DOI?
Keywords
Emulsifier, Sample Entropy, EMD, BP Neural Network.
Abstract
Aiming at the nonlinear characteristics of the fault vibration signal of the emulsifier, a feature extraction method based on Empirical Mode Decomposition (EMD) and Sample Entropy (SampEn) is proposed.In this method, the original vibration signal is decomposed into a finite number of intrinsic mode functions by EMD, then we select the Sample Entropy (SampEn) of the intrinsic mode function which contains the main failure information as the characteristic parameter, and BP neural network is used to diagnose the faults of the emulsifier in this study. Experiments verified that the BP neural network diagnosis method can get better fault diagnosis effect by using the EMD pretreatment to extract the Sample Entropy as the characteristic parameter.
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Proceedings
2016 4th International Conference on Advanced Materials and Information Technology Processing (AMITP 2016)
Part of series
Advances in Computer Science Research
Publication Date
September 2016
ISBN
978-94-6252-245-9
ISSN
2352-538X
DOI
https://doi.org/10.2991/amitp-16.2016.103How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Yue-sheng Wang
AU  - Yao-wen Sun
PY  - 2016/09
DA  - 2016/09
TI  - Study Of Vertical Emulsifier Fault Diagnosis System Based On EMD-Sample Entropy And BP Neural Network
BT  - 2016 4th International Conference on Advanced Materials and Information Technology Processing (AMITP 2016)
PB  - Atlantis Press
SP  - 519
EP  - 523
SN  - 2352-538X
UR  - https://doi.org/10.2991/amitp-16.2016.103
DO  - https://doi.org/10.2991/amitp-16.2016.103
ID  - Wang2016/09
ER  -