Feature selection techniques for increasing reliability of fault diagnosis of bearings

MdR. Islam, M.M.M. Islam, J.-M. Kim

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

11 Citations (Scopus)

Abstract

Features selection (FS) techniques have an apparent need in many complex engineering applications especially the bearing fault diagnosis of low-speed industrial motor. The main goal of an FS algorithm is to select the most discriminant features subset from a high-dimension features vector that increases the model performance by reducing the redundant and irrelevant fault features. This paper proposes an efficient fault diagnosis model of bearing by incorporating the optimal feature selection approach for increasing the reliability of fault diagnosis of bearing. Also, this paper investigates the feature selection approaches including sequential forward selection (SFS), sequential floating forward selection (SFFS), and genetic algorithm (GA) for identifying the most discriminant subset. The effectiveness of this discriminant features subset is verified with a low-speed bearing fault diagnosis application for identifying bearing failures. The experimental shows up-to-mark diagnosis performance using GA based optimal feature selection method.
Original languageEnglish
Title of host publicationProceedings of 9th International Conference on Electrical and Computer Engineering, ICECE 2016
PublisherIEEE
Pages1-4
Number of pages4
ISBN (Electronic)978-1-5090-2963-1, 978-1-5090-2962-4
ISBN (Print)978-1-5090-2964-8
DOIs
Publication statusPublished (in print/issue) - 16 Feb 2017
Event9th International Conference on Electrical and Computer Engineering - Dhaka, Bangladesh
Duration: 20 Dec 201622 Dec 2016
Conference number: 2016
https://ieeexplore.ieee.org/xpl/conhome/7845247/proceeding

Conference

Conference9th International Conference on Electrical and Computer Engineering
Abbreviated titleICECE
Country/TerritoryBangladesh
CityDhaka
Period20/12/1622/12/16
Internet address

Keywords

  • Acoustic emission (AE)
  • signal processing
  • feature selection
  • machine learning
  • Fault detection and diagnosis

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