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Roughness Scaling Extraction Accelerated by Dichotomy-Binary Strategy and Its Application to Milling Vibration Signal

Author

Listed:
  • Feng Feng

    (Division of Advanced Manufacturing, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China)

  • Meng Yuan

    (Division of Advanced Manufacturing, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China)

  • Yousheng Xia

    (Division of Advanced Manufacturing, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China)

  • Haoming Xu

    (Division of Advanced Manufacturing, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China)

  • Pingfa Feng

    (Division of Advanced Manufacturing, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
    Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China)

  • Xinghui Li

    (Division of Advanced Manufacturing, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
    Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen 518055, China)

Abstract

Fractal algorithms for signal analysis are developed from geometric fractals and can be used to describe various complex signals in nature. A roughness scaling extraction algorithm with first-order flattening (RSE-f1) was shown in our previous studies to have a high accuracy, strong noise resistance, and a unique capacity to recognize the complexity of non-fractals that are common in signals. In this study, its disadvantage of a long calculation duration was addressed by using a dichotomy-binary strategy. The accelerated RSE-f1 algorithm (A-RSE-f1) retains the three above-mentioned advantages of the original algorithm according to theoretical analysis and artificial signal testing, while its calculation speed is significantly accelerated by 13 fold, which also makes it faster than the typical Higuchi algorithm. Afterwards, the vibration signals of the milling process are analyzed using the A-RSE-f1 algorithm, demonstrating the ability to distinguish different machining statuses (idle, stable, and chatter) effectively. The results of this study demonstrate that the RSE algorithm has been improved to meet the requirements of practical engineering with both a fast speed and a high performance.

Suggested Citation

  • Feng Feng & Meng Yuan & Yousheng Xia & Haoming Xu & Pingfa Feng & Xinghui Li, 2022. "Roughness Scaling Extraction Accelerated by Dichotomy-Binary Strategy and Its Application to Milling Vibration Signal," Mathematics, MDPI, vol. 10(7), pages 1-17, March.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:7:p:1105-:d:782401
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    References listed on IDEAS

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    1. Ahmed Ragab & Soumaya Yacout & Mohamed-Salah Ouali & Hany Osman, 2019. "Prognostics of multiple failure modes in rotating machinery using a pattern-based classifier and cumulative incidence functions," Journal of Intelligent Manufacturing, Springer, vol. 30(1), pages 255-274, January.
    2. Zou, Mingqing & Yu, Boming & Feng, Yongjin & Xu, Peng, 2007. "A Monte Carlo method for simulating fractal surfaces," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 386(1), pages 176-186.
    3. Zuo, Xue & Tang, Xiang & Zhou, Yuankai, 2020. "Influence of sampling length on estimated fractal dimension of surface profile," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    4. M. Pour & M. A. Torabizadeh, 2016. "Improved prediction of stability lobes in milling process using time series analysis," Journal of Intelligent Manufacturing, Springer, vol. 27(3), pages 665-677, June.
    5. Yao, Kui & Chen, Haotian & Peng, W.L. & Wang, Zekun & Yao, Jia & Wu, Yipeng, 2021. "A new method on Box dimension of Weyl-Marchaud fractional derivative of Weierstrass function," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    6. Chen, Zhiying & Liu, Yong & Zhou, Ping, 2018. "A comparative study of fractal dimension calculation methods for rough surface profiles," Chaos, Solitons & Fractals, Elsevier, vol. 112(C), pages 24-30.
    7. Liu, Yao & Wang, Yashun & Chen, Xun & Yu, Huangchao, 2018. "A spherical conformal contact model considering frictional and microscopic factors based on fractal theory," Chaos, Solitons & Fractals, Elsevier, vol. 111(C), pages 96-107.
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    Cited by:

    1. Camelia Petrescu & Valeriu David, 2022. "Preface to the Special Issue on “Modelling and Simulation in Engineering”," Mathematics, MDPI, vol. 10(14), pages 1-3, July.

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