A Novel Fault Diagnosis method for Rotating Machinery of Imbalanced Data

Qi Han, Xianghua Wang, Rui Yang

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

6 Citations (Scopus)

Abstract

In this paper, a novel classification approach for imbalanced data with high-dimensional and intra-class imbalance is proposed, and they applied to fault diagnosis of rotating machinery. It is noted that the most of existed work on imbalanced learning focus on the inter-class imbalance, and ignore the intra-class imbalance. To solve the classification of imbalanced data with high-dimensional and intra-class imbalance, we proposed an integrated data-based and feature-based algorithm, which combines hybrid feature dimensionality reduction with a varied density based safe level synthetic minority oversampling technique (VDB-SLSMOTE), transforming the imbalanced data into balanced data. The balanced data is classified by random forest, and the final experimental result verified the effectiveness of the algorithm.

Original languageEnglish
Title of host publicationProceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2072-2077
Number of pages6
ISBN (Electronic)9781665440899
DOIs
Publication statusPublished - 2021
Event33rd Chinese Control and Decision Conference, CCDC 2021 - Kunming, China
Duration: 22 May 202124 May 2021

Publication series

NameProceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021

Conference

Conference33rd Chinese Control and Decision Conference, CCDC 2021
Country/TerritoryChina
CityKunming
Period22/05/2124/05/21

Keywords

  • high-dimensional and intra-class imbalance
  • hybrid feature dimensionality reduction
  • imbalanced data
  • rotating machinery
  • varied density based safe level synthetic minority oversampling technique

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