Fault Diagnosis of Intershaft Bearing Using Variational Mode Decomposition with TAGA Optimization

To efficiently extract the features of aeroengine intershaft bearing faults with weak signal, the variational mode decomposition (VMD) method based on the tolerant adaptive genetic algorithm (TAGA) (TAGA-VMD) is proposed by introducing the idea of tolerance into the traditional adaptive genetic algo...

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Bibliographic Details
Published in:Shock and vibration 2021, Vol.2021 (1)
Main Authors: Tian, Jing, Wang, Shu-Guang, Zhou, Jie, Ai, Yan-Ting, Zhang, Yu-Wei, Fei, Cheng-Wei
Format: Article
Language:English
Subjects:
Adaptive algorithms
Aerospace engines
Algorithms
Bearings
Decomposition
Efficiency
Fault diagnosis
Feature extraction
Genetic algorithms
Lagrange multiplier
Noise
Optimization
Optimization algorithms
Signal processing
Simulation methods
Spectrum allocation
Wavelet transforms
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Summary:To efficiently extract the features of aeroengine intershaft bearing faults with weak signal, the variational mode decomposition (VMD) method based on the tolerant adaptive genetic algorithm (TAGA) (TAGA-VMD) is proposed by introducing the idea of tolerance into the traditional adaptive genetic algorithm in this paper. In this method, the tolerant genetic algorithm was adopted to find the optimum empirical parameters K and α of VMD. A fault simulation experiment system of intershaft bearings was built for the inner ring fault and outer ring fault of bearings to verify the proposed TAGA-VMD method. The results show that the proposed method can effectively extract the fault feature frequency of intershaft bearings, and the error between the extracted fault feature frequency and the theoretical value of fault frequency is less than 0.1%. The efforts of this study provide one promising fault feature extraction approach for aeroengine intershaft bearing fault diagnosis with weak signal.
ISSN:1070-9622
1875-9203
DOI:10.1155/2021/8828317