Research on Gearbox Fault Detection and Diagnosis Based on Improved Spectral Kurtosis Algorithm
Lijun Cao, Yanqin Zhao, Guibo Yu, Shuxiao Chen, Xujun Su
Available Online July 2016.
- https://doi.org/10.2991/mcae-16.2016.41How to use a DOI?
- spectral kurtosis; minimum entropy deconvolution; gearbox; fault detection; fault diagnosis
- 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.
- 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 -