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Wind Turbine Gearbox Bearing Fault Diagnosis Method Based on ICEEMDAN and Flexible Wavelet Threshold

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Abstract

Extracting eigenvalues from vibration pulse signals is a crucial aspect of diagnosing faults in wind turbine gearbox bearings. However, the presence of ambient noise and high-frequency harmonics in the vibration signals brings a challenge in identifying fault information. This study proposes a malfunction diagnostic research approach based on the ICEEMDAN–flexible wavelet threshold to accurately and thoroughly identify malfunction feature messages from vibration signals. The approach decomposes the vibration signal into multiple intrinsic mode function (IMF) constituents using intrinsic computing expressive empirical mode decomposition with adaptive noise (ICEEMDAN). The correlation coefficient method is then utilized to identify noise-focused and message-focused IMF constituents, with the message-focused IMF constituents being retained and the noise-focused IMF constituents undergoing ICEEMDAN decomposition again. The scale function for each component is gained by detrended fluctuation analysis (DFA). Subsequently, a flexible wavelet threshold is applied to the noise-focused IMF constituent produced by the second stage of decomposition to extract meaningful signal messages. Ultimately, the message-focused IMF constituent produced by both stages of decomposition and the obtained signal of wavelet noise reduction yields a reconstructed signal. The experimental and simulation results show that the SNR of the flexible wavelet threshold is 37.09, which is 3.9 and 7.6% higher than the hard and soft thresholds, respectively. Meanwhile, the MSE of flexible wavelet thresholding is 0.0019, which is reduced by 26.9 and 44.1% compared with hard and soft thresholding, respectively. Therefore, the proposed method in the paper can effectively retrieve signal messages, and the comparison with the traditional method indicates the advantage of the approach proposed in the research for wind turbine gearbox bearing malfunction diagnostic.

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Abbreviations

IMF:

Intrinsic mode function

DFA:

Detrended fluctuation analysis

EMD:

Empirical modal decomposition

DWT:

Discrete wavelet transform

SVD:

Single value decomposition

HHT:

Hilbert–Huang transform

EEMD:

Ensemble empirical mode decomposition

CEEMD:

Complementary ensemble empirical mode decomposition

CEEMDAN:

Complementary ensemble empirical mode decomposition with adaptive noise

ICEEMDAN:

Intrinsic computing expressive empirical mode decomposition with adaptive noise

SNR:

Signal–noise ratio

MSE:

Mean squared error

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Acknowledgments

The research was supported by the National Natural Science Foundation of China (No. 51675350).

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Authors and Affiliations

  1. School of Mechanical Engineering, Shenyang University of Technology, Shenyang, China

    Lu Gao, YanLing Gu, Changzheng Chen, Pinyang Zhang & Zipei Zhang

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  1. Lu Gao
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Correspondence to YanLing Gu.

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Gao, L., Gu, Y., Chen, C. et al. Wind Turbine Gearbox Bearing Fault Diagnosis Method Based on ICEEMDAN and Flexible Wavelet Threshold. J Fail. Anal. and Preven. (2024). https://doi.org/10.1007/s11668-024-01899-6

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