Classification of ball bearing faults using a hybrid intelligent model

Manjeevan Seera, M. L.Dennis Wong, Asoke K. Nandi

Research output: Contribution to journalArticleResearchpeer-review

37 Citations (Scopus)

Abstract

In this paper, classification of ball bearing faults using vibration signals is presented. A review of condition monitoring using vibration signals with various intelligent systems is first presented. A hybrid intelligent model, FMM-RF, consisting of the Fuzzy Min-Max (FMM) neural network and the Random Forest (RF) model, is proposed. A benchmark problem is tested to evaluate the practicality of the FMM-RF model. The proposed model is then applied to a real-world dataset. In both cases, power spectrum and sample entropy features are used for classification. Results from both experiments show good accuracy achieved by the proposed FMM-RF model. In addition, a set of explanatory rules in the form of a decision tree is extracted to justify the predictions. The outcomes indicate the usefulness of FMM-RF in performing classification of ball bearing faults.

Original languageEnglish
Pages (from-to)427-435
Number of pages9
JournalApplied Soft Computing
Volume57
DOIs
Publication statusPublished - Aug 2017
Externally publishedYes

Keywords

  • Ball bearing
  • Condition monitoring
  • Electrical motor
  • Fuzzy min-max neural network
  • Random forest

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