IGIgram: An Improved Gini Index-Based Envelope Analysis for Rolling Bearing Fault Diagnosis

Authors

  • Bingyan Chen State Key Laboratory of Traction Power, Southwest Jiaotong University, China https://orcid.org/0000-0001-7103-0221
  • Dongli Song State Key Laboratory of Traction Power, Southwest Jiaotong University, China
  • Yao Cheng State Key Laboratory of Traction Power, Southwest Jiaotong University, China
  • Weihua Zhang State Key Laboratory of Traction Power, Southwest Jiaotong University, China
  • Baoshan Huang School of Industrial Automation, Beijing Institute of Technology, China
  • Yousif Muhamedsalih Institute of Railway Research, University of Huddersfield, UK

DOI:

https://doi.org/10.37965/jdmd.2022.65

Keywords:

envelope analysis; fault diagnosis; frequency band identification; improved Gini indices; railway bearings

Abstract

The transient impulse features caused by rolling bearing faults are often present in the resonance frequency band which is closely related to the dynamic characteristics of the machine structure. Informative frequency band identification is a crucial prerequisite for envelope analysis and thereby accurate fault diagnosis of rolling bearings. In this paper, based on the ratio of quasi-arithmetic means and Gini index, improved Gini indices (IGIs) are proposed to quantify the transient impulse features of a signal, and their effectiveness and advantages in sparse quantification are confirmed by simulation analysis and comparisons with traditional sparsity measures. Furthermore, an IGI-based envelope analysis method named IGIgram is developed for fault diagnosis of rolling bearings. In the new method, an IGI-based indicator is constructed to evaluate the impulsiveness and cyclostationarity of the narrow-band filtered signal simultaneously, and then a frequency band with abundant fault information is adaptively determined for extracting bearing fault features. The performance of the IGIgram method is verified on the simulation signal and railway bearing experimental signals and compared with typical sparsity measures-based envelope analysis methods and log-cycligram. The results demonstrate that the proposed IGIs are efficient in quantifying bearing fault-induced transient features and the IGIgram method with appropriate power exponent can effectively achieve the diagnostics of different axle-box bearing faults.

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Published

2022-06-10

How to Cite

Chen, B., Song, D., Cheng, Y., Zhang, W., Huang, B., & Muhamedsalih, Y. (2022). IGIgram: An Improved Gini Index-Based Envelope Analysis for Rolling Bearing Fault Diagnosis. Journal of Dynamics, Monitoring and Diagnostics, 111–124. https://doi.org/10.37965/jdmd.2022.65

Issue

Section

Regular Article