Bearing faults diagnosis and classification using Generalized Gaussian Distribution multiscale dispersion entropy features

Ragavesh Dhandapani, Imene Mitiche, Scott McMeekin, Gordon Morison

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)


Effective fault diagnosis of rolling bearings are vital for the reliable and smooth operation of industrial equipment. Early fault detection and diagnosis of rolling bearings are required to avoid catastrophic failures and financial losses. In this paper, we propose a new sophisticated Multiscale Dispersion Entropy (MDE) based feature that uses a nonlinear mapping approach using a Generalized Gaussian Distribution (GGD)-Cumulative Distribution Function (CDF). First of all, the proposed feature extraction method is used to extract the features from a raw 1-D vibration signal and the candidate feature of each vibration signal is selected by analysing the standard deviation of the features. Then, the features are used as input to a Multi-class Support Vector Machine (MCSVM) model for categorizing rolling bearing fault conditions. The findings demonstrate that the proposed method is better in terms of classification accuracy, precision, recall and F1-score as compared to other entropy feature driven classification models.

Original languageEnglish
Title of host publication30th European Signal Processing Conference (EUSIPCO 2022): Proceedings
Number of pages5
ISBN (Electronic)9789082797091
ISBN (Print)9781665467995
Publication statusPublished - 18 Oct 2022
Event30th European Signal Processing Conference - Crowne Plaza, Belgrade, Serbia
Duration: 29 Aug 20222 Sept 2022 (Link to conference website)

Publication series

ISSN (Print)2219-5491
ISSN (Electronic)2076-1465


Conference30th European Signal Processing Conference
Abbreviated titleEUSIPCO 2022
Internet address


  • Bearing Fault Classification
  • Dispersion Entropy
  • Generalized Gaussian Distribution
  • Multi-class Support Vector Machine
  • Multiscale Dispersion Entropy

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering


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