An improved random forest algorithm of fault diagnosis for rotating machinery

Zilan Wang, Maiying Zhong*, Rui Yang, Yang Liu

*Corresponding author for this work

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

1 Citation (Scopus)

Abstract

In this paper, a semi-supervised random forest (RF) algorithm is presented for fault diagnosis of rotating machinery. Firstly, many unlabeled samples are divided into two parts, denoted respectively as unlabeled sample I and unlabeled sample II. Then a graph-all the labeled samples are used to train the multiple decision trees. If the classification result is consistent with the one of label prediction, then the unlabeled sample I is added to the labeled samples and used for building RF model. While, the data of unlabeled sample II are utilized for testing of the obtained RF model. Finally, the developed RF algorithm is applied to an experimental platform of rotating machinery. It is shown from the simulation results that, for the cases of noisy samples with unsatisfying labels, the new developed semi-supervised RF algorithm can improve the fault classification accuracy than the conventional RF.

Original languageEnglish
Title of host publicationProceedings of 2019 11th CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes, SAFEPROCESS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages12-17
Number of pages6
ISBN (Electronic)9781728106816
DOIs
Publication statusPublished - Jul 2019
Event11th CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes, SAFEPROCESS 2019 - Xiamen, China
Duration: 5 Jul 20197 Jul 2019

Publication series

NameProceedings of 2019 11th CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes, SAFEPROCESS 2019

Conference

Conference11th CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes, SAFEPROCESS 2019
Country/TerritoryChina
CityXiamen
Period5/07/197/07/19

Keywords

  • Fault Classification
  • Fault Diagnosis
  • Random Forest
  • Rotating Machinery
  • Semi-supervised Learning

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