Adaptive Joint Denoising Method of Rolling Bearing using Improved VMD and MOMEDA

LUO Shimin; HUANG Jiezhou; CAI Binghuan

doi:10.13433/j.cnki.1003-8728.20200346

Volume 41 Issue 3

May 2022

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Article Navigation > Mechanical Science and Technology for Aerospace Engineering > 2022 > 41(3): 329-336

LUO Shimin, HUANG Jiezhou, CAI Binghuan. Adaptive Joint Denoising Method of Rolling Bearing using Improved VMD and MOMEDA[J]. Mechanical Science and Technology for Aerospace Engineering, 2022, 41(3): 329-336. doi: 10.13433/j.cnki.1003-8728.20200346

Citation:

LUO Shimin, HUANG Jiezhou, CAI Binghuan. Adaptive Joint Denoising Method of Rolling Bearing using Improved VMD and MOMEDA[J]. Mechanical Science and Technology for Aerospace Engineering, 2022, 41(3): 329-336. doi: 10.13433/j.cnki.1003-8728.20200346

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Adaptive Joint Denoising Method of Rolling Bearing using Improved VMD and MOMEDA

doi: 10.13433/j.cnki.1003-8728.20200346

School of Mechatronics & Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China

Received Date: 2020-09-15
Available Online: 2022-05-06
Publish Date: 2022-05-11

Abstract

Abstract

When a local fault occurs in the bearing, the weak impact characteristic that can represent the early fault of the rolling bearing is often submerged by strong background noise in the process of sensor acquisition, and it is easily affected by the signal transmission path, thus making it difficult to diagnose the bearing fault. To solve the above problems, an adaptive combined rolling bearing denoising method based on improved variational mode decomposition (VMD) and multi-point optimal minimum entropy deconvolution (MOMEDA) is proposed in this paper. Firstly, in order to solve the problem of VMD which relies heavily on artificial prior knowledge, particle swarm optimization (PSO) is adopted to optimize VMD, and kurtosis is taken as the optimization index to select the optimal IMF component, and then MOMEDA is further adopted to eliminate the influence of the transmission path on the signal. Finally, the rolling bearing fault is diagnosed combined with 1.5D energy spectrum. Compared with MED-VMD and conventional envelope spectrum methods, the advantages of the proposed method are proved in the field of bearing fault feature extraction.
- kurtosis,
- particle swarm optimization,
- variational mode decomposition,
- 1.5D energy spectrum

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References(17)

References

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