Multi-stage feature selection by using genetic algorithms for fault diagnosis in gearboxes based on vibration signal

Research output: Contribution to journalArticlepeer-review

75 Scopus citations

Abstract

There are growing demands for condition-based monitoring of gearboxes, and techniques to improve the reliability, effectiveness and accuracy for fault diagnosis are considered valuable contributions. Feature selection is still an important aspect in machine learning-based diagnosis in order to reach good performance in the diagnosis system. The main aim of this research is to propose a multi-stage feature selection mechanism for selecting the best set of condition parameters on the time, frequency and time-frequency domains, which are extracted from vibration signals for fault diagnosis purposes in gearboxes. The selection is based on genetic algorithms, proposing in each stage a new subset of the best features regarding the classifier performance in a supervised environment. The selected features are augmented at each stage and used as input for a neural network classifier in the next step, while a new subset of feature candidates is treated by the selection process. As a result, the inherent exploration and exploitation of the genetic algorithms for finding the best solutions of the selection problem are locally focused. The approach is tested on a dataset from a real test bed with several fault classes under different running conditions of load and velocity. The model performance for diagnosis is over 98%.

Original languageEnglish
Pages (from-to)23903-23926
Number of pages24
JournalSensors (Switzerland)
Volume15
Issue number9
DOIs
StatePublished - 18 Sep 2015

Bibliographical note

Publisher Copyright:
© 2015 by the authors; licensee MDPI, Basel, Switzerland.

Keywords

  • Fault diagnosis
  • Feature selection
  • Gearbox
  • Genetic algorithms
  • Neural networks
  • Vibration signal

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