Identification of Gear Fault Signal Based on Adaptive EMD Feature Extraction and Optimal GA-LSSVM Classification Model

  • VietHung Nguyen
  • TienDung Hoang
  • VanTrong Thai
  • QuocTuan Nguyen
  • XuanChung Nguyen
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 104)


The paper proposed a new efficient diagnostic method for the various fault statuses of gear. This method identifies the gear fault exactly by an optimal classification model based on our relevant features. Firstly, the empirical mode decomposition (EMD) method is used to decompose a non-linear and non-stationary vibration signal into intrinsic mode functions (IMFs). The statistical characteristics of the first several IMFs are calculated to form a feature-matrix. Secondly, the meaningful singular values are exploited by the singular value decomposition (SVD) method, which represent as an important feature vector of the original vibration signal, named EMD-SVD. The obtained EMD-SVD feature is the necessary input to serve the classifier model. Finally, a least square support vector machine model (LSSVM) is optimally trained in the EMD-SVD feature, learning to identify the respective fault pattern with the various status of gear. In the formation of the optimal classifier model, the genetic algorithm is used to find out the best parameter pair of the LSSVM classifier model, commonly called GA-LSSVM. We experimented on gear vibration dataset, and the achieved results demonstrated that the proposed diagnostic method was effective in terms of identification accuracy in the short time.


Gear fault Diagnosis Least square support vector machine (LSSVM) Statistical characteristics Genetic algorithm (GA) 



The authors would like to express our gratitude to Faculty of Mechanical Engineering, Hanoi University of Industry and College of Mechanical and Vehicle Engineering, Hunan University for their support.

Conflicts of Interest

The authors declare that they have no conflict of interest.


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • VietHung Nguyen
    • 1
    • 2
  • TienDung Hoang
    • 1
  • VanTrong Thai
    • 1
    • 2
  • QuocTuan Nguyen
    • 1
  • XuanChung Nguyen
    • 3
  1. 1.Faculty of Mechanical EngineeringHanoi University of IndustryHanoiVietnam
  2. 2.College of Mechanical and Vehicle EngineeringHunan UniversityChangshaChina
  3. 3.VietNam-Japan Center of Mechanical Engineering, Hanoi University of IndustryHanoiVietnam

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