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

Yan Shen, Ping Wang, Xuesong Wang and Ke Sun
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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

Energies, 2021, vol. 14, issue 22, 1-16

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.

Keywords: surface vibration signal; improved empirical mode decomposition; extreme learning machine (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
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