Proceedings of the International Conference on Logistics, Engineering, Management and Computer Science

An Improved EMD with Second Generation Wavelet and Feature Extraction for Fault Diagnosis of Rotating Machinery

Authors
Fengli Wang, Sihong Li, Hui Xing, Qinan Liu
Corresponding Author
Fengli Wang
Available Online July 2015.
DOI
https://doi.org/10.2991/lemcs-15.2015.38How to use a DOI?
Keywords
EMD; Fault Diagnosis; Feature Extraction; Second Generation Wavelet; Rotating Machinery
Abstract
Fault feature extraction is a challenge for fault diagnosis of rotating machinery. The vibration signals measured from rotating machinery are usually non-stationary and nonlinear. Especially, the useful fault characteristics are too weak to be identified at the early stage. In order to solve the problem, a novel method called improved empirical mode decomposition (EMD) with second generation wavelet for fault diagnosis of rotating machinery is proposed. According to the local characteristics of vibration signal and selecting the proper criterion of minimizing the squared error, an optimal predicting operator is constructed for a transforming sample, so that the second generation wavelet basis function is able to fit the local characteristics of the vibration signal. Using the self-adaptive second generation wavelet as the pre-filter to improve EMD decomposition results, EMD is further improved to increase the accuracy and effectiveness of the decomposition results. The proposed method is applied to analyze the rub-impact rotor experimental setup, and the results show that the proposed method is accurate and efficient, and is expected to be applied in engineering practice effectively.
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Proceedings
International Conference on Logistics Engineering, Management and Computer Science (LEMCS 2015)
Part of series
Advances in Intelligent Systems Research
Publication Date
July 2015
ISBN
978-94-6252-102-5
ISSN
1951-6851
DOI
https://doi.org/10.2991/lemcs-15.2015.38How 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  - Fengli Wang
AU  - Sihong Li
AU  - Hui Xing
AU  - Qinan Liu
PY  - 2015/07
DA  - 2015/07
TI  - An Improved EMD with Second Generation Wavelet and Feature Extraction for Fault Diagnosis of Rotating Machinery
BT  - International Conference on Logistics Engineering, Management and Computer Science (LEMCS 2015)
PB  - Atlantis Press
SN  - 1951-6851
UR  - https://doi.org/10.2991/lemcs-15.2015.38
DO  - https://doi.org/10.2991/lemcs-15.2015.38
ID  - Wang2015/07
ER  -