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An Improved Signal Processing Approach Based on Analysis Mode Decomposition and Empirical Mode Decomposition

Author

Listed:
  • Zhongzhe Chen

    (The School of Mechanical and Electrical Engineering, University of Electronic and Science Technology of China, Chengdu 611731, China)

  • Baqiao Liu

    (Department of Computer Science, University of North Carolina, Chapel Hill, NC 27516, USA)

  • Xiaogang Yan

    (The School of Mechanical and Electrical Engineering, University of Electronic and Science Technology of China, Chengdu 611731, China)

  • Hongquan Yang

    (The School of Mechanical and Electrical Engineering, University of Electronic and Science Technology of China, Chengdu 611731, China)

Abstract

Empirical mode decomposition (EMD) is a widely used adaptive signal processing method, which has shown some shortcomings in engineering practice, such as sifting stop criteria of intrinsic mode function (IMF), mode mixing and end effect. In this paper, an improved sifting stop criterion based on the valid data segment is proposed, and is compared with the traditional one. Results show that the new sifting stop criterion avoids the influence of end effects and improves the correctness of the EMD. In addition, a novel AEMD method combining the analysis mode decomposition (AMD) and EMD is developed to solve the mode-mixing problem, in which EMD is firstly applied to dispose the original signal, and then AMD is used to decompose these mixed modes. Then, these decomposed modes are reconstituted according to a certain principle. These reconstituted components showed mode mixing phenomena alleviated. Model comparison was conducted between the proposed method with the ensemble empirical mode decomposition (EEMD), which is the mainstream method improved based on EMD. Results indicated that the AEMD and EEMD can effectively restrain the mode mixing, but the AEMD has a shorter execution time than that of EEMD.

Suggested Citation

  • Zhongzhe Chen & Baqiao Liu & Xiaogang Yan & Hongquan Yang, 2019. "An Improved Signal Processing Approach Based on Analysis Mode Decomposition and Empirical Mode Decomposition," Energies, MDPI, vol. 12(16), pages 1-12, August.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:16:p:3077-:d:256380
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    References listed on IDEAS

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    1. Zhongzhe Chen & Shuchen Cao & Zijian Mao, 2017. "Remaining Useful Life Estimation of Aircraft Engines Using a Modified Similarity and Supporting Vector Machine (SVM) Approach," Energies, MDPI, vol. 11(1), pages 1-14, December.
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    Cited by:

    1. Nam, KiJeon & Heo, SungKu & Li, Qian & Loy-Benitez, Jorge & Kim, MinJeong & Park, DuckShin & Yoo, ChangKyoo, 2020. "A proactive energy-efficient optimal ventilation system using artificial intelligent techniques under outdoor air quality conditions," Applied Energy, Elsevier, vol. 266(C).

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