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Application of Empirical Mode Decomposition and Extreme Learning Machine Algorithms on Prediction of the Surface Vibration Signal

Author

Listed:
  • Yan Shen

    (College of Mathematical Sciences, Harbin Engineering University, Harbin 150001, China)

  • Ping Wang

    (College of Mathematical Sciences, Harbin Engineering University, Harbin 150001, China)

  • Xuesong Wang

    (College of Mathematical Sciences, Harbin Engineering University, Harbin 150001, China)

  • Ke Sun

    (College of Shipbuilding Engineering, Harbin Engineering University, Harbin 150001, China)

Abstract

Accurately predicting surface vibration signals of diesel engines is the key to evaluating the operation quality of diesel engines. Based on an improved empirical mode decomposition and extreme learning machine algorithm, the characteristics of diesel engine surface vibration signal were detected, predicted, and analyzed. First, the surface vibration signal was decomposed into a series of signal components by an improved empirical mode decomposition algorithm. Then, the extreme learning machine algorithm was applied to each signal component to obtain the predicted value of the corresponding signal component and determine the characteristics of the ground vibration signal. Compared with the empirical mode decomposition–extremum learning machine algorithm and the extremum learning machine algorithm, the results show that the improved empirical mode decomposition–extremum learning machine algorithm is feasible and effective.

Suggested Citation

  • Yan Shen & Ping Wang & Xuesong Wang & Ke Sun, 2021. "Application of Empirical Mode Decomposition and Extreme Learning Machine Algorithms on Prediction of the Surface Vibration Signal," Energies, MDPI, vol. 14(22), pages 1-16, November.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:22:p:7519-:d:676507
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    References listed on IDEAS

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    1. Tim Leung & Theodore Zhao, 2021. "Financial Time Series Analysis and Forecasting with HHT Feature Generation and Machine Learning," Papers 2105.10871, arXiv.org.
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