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Intelligent Algorithm for Variable Scale Adaptive Feature Separation of Mechanical Composite Fault Signals

Author

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  • Shu Han

    (School of Microelectronics and Communications Engineering, Chongqing University, Chongqing 400030, China
    Current address: School of Microelectronics and Communications Engineering, Chongqing University, Chongqing, China.
    These authors contributed equally to this work.)

  • Xiaoming Liu

    (School of Microelectronics and Communications Engineering, Chongqing University, Chongqing 400030, China
    These authors contributed equally to this work.)

  • Yan Yang

    (School of Microelectronics and Communications Engineering, Chongqing University, Chongqing 400030, China
    These authors contributed equally to this work.)

  • Hailin Cao

    (School of Microelectronics and Communications Engineering, Chongqing University, Chongqing 400030, China
    These authors contributed equally to this work.)

  • Yuanhong Zhong

    (School of Microelectronics and Communications Engineering, Chongqing University, Chongqing 400030, China
    These authors contributed equally to this work.)

  • Chuanlian Luo

    (School of Microelectronics and Communications Engineering, Chongqing University, Chongqing 400030, China
    These authors contributed equally to this work.)

Abstract

With the development of modern industry and scientific technology, production equipment plays an increasingly important role in military and industrial production, and the fault detection signal of gears and bearings state in transmission equipment becomes very important. Therefore, this paper proposes a gear-bearing composite fault signal decomposition and reconstruction method, which combines the marine predator algorithm (MPA) and variational mode decomposition (VMD) technologies. For the parameters’ selection of VMD, the optimization algorithm allows us to quickly and accurately obtain the results with the best kurtosis correlation index after signal decomposition and reconstruction. The experiments demonstrate the excellent performance of our method in the field of separation and denoising mixed gear-bearing fault signals.

Suggested Citation

  • Shu Han & Xiaoming Liu & Yan Yang & Hailin Cao & Yuanhong Zhong & Chuanlian Luo, 2021. "Intelligent Algorithm for Variable Scale Adaptive Feature Separation of Mechanical Composite Fault Signals," Energies, MDPI, vol. 14(22), pages 1-13, November.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:22:p:7702-:d:681350
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    References listed on IDEAS

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    1. G. M. Viswanathan & Sergey V. Buldyrev & Shlomo Havlin & M. G. E. da Luz & E. P. Raposo & H. Eugene Stanley, 1999. "Optimizing the success of random searches," Nature, Nature, vol. 401(6756), pages 911-914, October.
    2. Farid Aubras & Cedric Damour & Michel Benne & Sebastien Boulevard & Miloud Bessafi & Brigitte Grondin-Perez & Amangoua J.-J. Kadjo & Jonathan Deseure, 2021. "A Non-Intrusive Signal-Based Fault Diagnosis Method for Proton Exchange Membrane Water Electrolyzer Using Empirical Mode Decomposition," Energies, MDPI, vol. 14(15), pages 1-11, July.
    3. Abdenour Soualhi & Bilal El Yousfi & Hubert Razik & Tianzhen Wang, 2021. "A Novel Feature Extraction Method for the Condition Monitoring of Bearings," Energies, MDPI, vol. 14(8), pages 1-23, April.
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    Cited by:

    1. Xiaohua Song & Jing Liu & Chaobo Chen & Song Gao, 2022. "Advanced Methods in Rotating Machines," Energies, MDPI, vol. 15(15), pages 1-3, July.

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