Applying Bayesian Optimization of Parameters of Tunable Quality-Factor Wavelet Transform to Bearing Fault

ZHANG Le; PENG Xianlong; ZHU Huashuang

doi:10.13433/j.cnki.1003-8728.20220270

Volume 43 Issue 3

Mar. 2024

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Article Navigation > Mechanical Science and Technology for Aerospace Engineering > 2024 > 43(3): 504-512

ZHANG Le, PENG Xianlong, ZHU Huashuang. Applying Bayesian Optimization of Parameters of Tunable Quality-Factor Wavelet Transform to Bearing Fault[J]. Mechanical Science and Technology for Aerospace Engineering, 2024, 43(3): 504-512. doi: 10.13433/j.cnki.1003-8728.20220270

Citation:

ZHANG Le, PENG Xianlong, ZHU Huashuang. Applying Bayesian Optimization of Parameters of Tunable Quality-Factor Wavelet Transform to Bearing Fault[J]. Mechanical Science and Technology for Aerospace Engineering, 2024, 43(3): 504-512. doi: 10.13433/j.cnki.1003-8728.20220270

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Applying Bayesian Optimization of Parameters of Tunable Quality-Factor Wavelet Transform to Bearing Fault

doi: 10.13433/j.cnki.1003-8728.20220270

1.
School of Mechanical Engineering, Xi'an University of Science and Technology, Xi'an 710054, China

Received Date: 2022-03-02
Publish Date: 2024-03-25

Abstract

Abstract

It is costly to use the grid search and optimization algorithm to tune the parameters of tunable quality-factor wavelet transform (TQWT). A method for bearing fault diagnosis based on the Bayesian optimization of TQWT parameters was proposed. The optimal solution of the entropy-kurtosis synthetic objective function was solved by using the Bayesian optimization algorithm in the space of TQWT parameters, according to which the TQWT parameters were set to decompose the original bearing fault signals. The sub-band signal with the minimum value of the entropy-kurtosis index was selected to reconstruct its feature signals with the inverse TQWT transform, and the signal was then processed with an envelope demodulation algorithm. The type of bearing fault was judged with the reconstructed feature signal envelope spectrum. The simulation results on the actually measured bearing vibration signals and their analysis show that the proposed method can accurately extract the characteristic frequency information on fault and diagnose bearing faults at an early stage.
- bayesian optimization,
- TQWT,
- entropy-kurtosis index,
- fault diagnosis

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References(17)

References

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