Proceedings of the 2016 International Conference on Mechatronics, Control and Automation Engineering

Research on Gearbox Fault Detection and Diagnosis Based on Improved Spectral Kurtosis Algorithm

Authors
Lijun Cao, Yanqin Zhao, Guibo Yu, Shuxiao Chen, Xujun Su
Corresponding Author
Lijun Cao
Available Online July 2016.
DOI
https://doi.org/10.2991/mcae-16.2016.41How to use a DOI?
Keywords
spectral kurtosis; minimum entropy deconvolution; gearbox; fault detection; fault diagnosis
Abstract
A gearbox fault detection and diagnosis test table of wheeled armored vehicles is designed and established. Typical faults and vibration experiments can be preset. Aiming to the d that traditional spectral kurtosis algorithm cannot be applied to gearbox fault signal feature extraction under strong noise interference, the minimum entropy deconvolution theory is adopted, and a new kind of gearbox fault diagnosis method based on MED and FSK is proposed, which realizes the single fault diagnosis of gears and bearings at different speed conditions, and also achieves good result for composite fault diagnosis of rolling bearing inner and outer rings. Compared with the traditional method of wavelet analysis and EMD, the proposed method for gearbox vibration signal has better noise reduction result.
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This is an open access article distributed under the CC BY-NC license.

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Volume Title
Proceedings of the 2016 International Conference on Mechatronics, Control and Automation Engineering
Series
Advances in Engineering Research
Publication Date
July 2016
ISBN
978-94-6252-237-4
ISSN
2352-5401
DOI
https://doi.org/10.2991/mcae-16.2016.41How 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  - Lijun Cao
AU  - Yanqin Zhao
AU  - Guibo Yu
AU  - Shuxiao Chen
AU  - Xujun Su
PY  - 2016/07
DA  - 2016/07
TI  - Research on Gearbox Fault Detection and Diagnosis Based on Improved Spectral Kurtosis Algorithm
BT  - Proceedings of the 2016 International Conference on Mechatronics, Control and Automation Engineering
PB  - Atlantis Press
SP  - 174
EP  - 177
SN  - 2352-5401
UR  - https://doi.org/10.2991/mcae-16.2016.41
DO  - https://doi.org/10.2991/mcae-16.2016.41
ID  - Cao2016/07
ER  -