Fault feature extraction of bearing rolling elements under complex transmission path

DENG Wu; LI Ling-feng; LI Wei-han; ZHAO Hui-min

doi:10.19818/j.cnki.1671-1637.2023.01.014

Volume 23 Issue 1

Feb. 2023

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Article Navigation > Journal of Traffic and Transportation Engineering > 2023 > 23(1): 184-194

DENG Wu, LI Ling-feng, LI Wei-han, ZHAO Hui-min. Fault feature extraction of bearing rolling elements under complex transmission path[J]. Journal of Traffic and Transportation Engineering, 2023, 23(1): 184-194. doi: 10.19818/j.cnki.1671-1637.2023.01.014

Citation:

DENG Wu, LI Ling-feng, LI Wei-han, ZHAO Hui-min. Fault feature extraction of bearing rolling elements under complex transmission path[J]. Journal of Traffic and Transportation Engineering, 2023, 23(1): 184-194. doi: 10.19818/j.cnki.1671-1637.2023.01.014

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Fault feature extraction of bearing rolling elements under complex transmission path

doi: 10.19818/j.cnki.1671-1637.2023.01.014

1.
College of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China
2.
College of Marine Electrical Engineering, Dalian Maritime University, Dalian 116026, Liaoning, China
3.
Engineering Training Center, Civil Aviation University of China, Tianjin 300300, China

Funds:

National Natural Science Foundation of China 51605068

National Natural Science Foundation of China 51475065

National Natural Science Foundation of China 61771087

More Information

Author Bio:
DENG Wu(1976-), male, professor, PhD, dw7689@163.com

ZHAO Hui-min(1977-), female, professor, PhD, hm_zhao1977@126.com
Received Date: 2022-09-21
Available Online: 2023-03-08
Publish Date: 2023-02-25

Abstract

Abstract

In order to eliminate the influence of complex transmission path on the vibration signal of bearing rolling elements and improve the ability of fault feature extraction, the problem of fault feature extraction of bearing rolling elements based on the variational mode decomposition (VMD), optimized maximum correlated kurtosis deconvolution (MCKD) and 1.5-dimensional spectrum was studied. The characteristics of original vibration signal of bearing rolling element and early fault signal and the influence of the complex transmission path on the vibration signal were analyzed. The VMD was employed to decompose the original vibration signal into a series of intrinsic mode functions (IMFs), and thus the frequency conversion component elimination method was proposed. Two IMFs components with large kurtosis values were selected for the signal reconstruction according to the kurtosis criterion. Based on the grid search method, the parameter optimization method of MCKD algorithm was studied to enhance the periodic fault characteristics of reconstructed signals and eliminate the influence of complex transmission path on the fault signal of bearing rolling elements. The 1.5-dimensional spectrum was used to analyze the reconstructed signals, and a new fault feature extraction method of bearing rolling elements under the complex transmission path was established, thus realizing their accurate fault diagnosis. In order to prove the effectiveness of the method, the data on the base roller of the bearing SKF6205 from Case Western Reserve University were selected for the was experimental verification and analysis. Experimental results show that the grid search method leads to the optimal parameter values (365, 85) of the filter length and the impact period in the MCKD algorithm. The optimized MCKD algorithm enhances the fault characteristics of the reconstructed signals, reduces the irrelevant frequency components, and significantly decreases the interference of other components. The proposed fault feature extraction method can effectively extract the fault feature frequency (140.6 Hz), double frequency (281.3 Hz), triple frequency (421.9 Hz) and all other components under the loads of 0, 735 and 1 470 W, and there is no influence from load conditions, thus eliminating the influence of complex transmission path on the fault feature extraction of bearing rollers. It follows that the proposed method can effectively solve the problems of the fault feature extraction and diagnosis of bearing rollers under the complex transmission path.
- mechanical engineering,
- complex transmission,
- bearing rolling element,
- feature extraction,
- variational mode decomposition,
- MCKD,
- 1.5-dimensional spectrum

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References(36)

References

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Fault feature extraction of bearing rolling elements under complex transmission path

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Fault feature extraction of bearing rolling elements under complex transmission path

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