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Fault Diagnosis of Planetary Gearboxes Based on Improved EEMD and Frequency Demodulation Analysis

  • Haohua Liu
  • Fangyi LiEmail author
  • Feng Yang
  • Yanyan Nie
  • Liming Wang
  • Shanshan Zhang
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 130)

Abstract

Ensemble empirical mode decomposition (EEMD) has been widely used in fault diagnosis of planetary gearboxes. However, artificially selecting the amplitude of white Gaussian noise (WGN) which is the crucial parameter of EEMD probably produces subjective errors. To alleviate this drawback, a self-adaptive EEMD method based on particle swarm optimization (PSO-EEMD) is proposed. This method takes the uniform distribution characteristic of signal extreme points as the fitness function of PSO in order to self-adaptively determine the optimal amplitude of WGN, which can improve the self-adaptability and decomposition precision of EEMD. Furthermore, utilizing the advantages of PSO-EEMD in extracting weak characteristics from noisy signals, a new fault diagnosis method of planetary gearboxes based on PSO-EEMD and frequency demodulation analysis is put forward. Simulation analysis results show that the PSO-EEMD method can self-adaptively determine the optimal amplitude of GWN according to the original signal characteristics and obtain more accurate intrinsic mode functions (IMFs) than the traditional EEMD, which illustrate the effectiveness and superiority of the proposed method. The fault diagnosis case of a planetary gearbox shows that the proposed fault diagnosis method based on PSO-EEMD and frequency demodulation analysis can effectively extract the characteristic frequency from the sun gear with a crack fault.

Keywords

Fault diagnosis Ensemble empirical mode decomposition Particle swarm optimization Planetary gearbox 

Notes

Acknowledgement

This research is supported by the Shandong Provincial Key Research and Development Plan, China (No. 2017GGX30148).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Haohua Liu
    • 1
  • Fangyi Li
    • 1
    Email author
  • Feng Yang
    • 1
  • Yanyan Nie
    • 1
  • Liming Wang
    • 1
  • Shanshan Zhang
    • 1
  1. 1.National Demonstration Center for Experimental Mechanical Engineering EducationShandong UniversityJinanChina

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