Autoregressive modelling for rolling element bearing fault diagnosis

H Al-Bugharbee, I Trendafilova

Research output: Contribution to journalConference Contribution

Abstract

In this study, time series analysis and pattern recognition analysis are used effectively for the purposes of rolling bearing fault diagnosis. The main part of the suggested methodology is the autoregressive (AR) modelling of the measured vibration signals. This study suggests the use of a linear AR model applied to the signals after they are stationarized. The obtained coefficients of the AR model are further used to form pattern vectors which are in turn subjected to pattern recognition for differentiating among different faults and different fault sizes. This study explores the behavior of the AR coefficients and their changes with the introduction and the growth of different faults. The idea is to gain more understanding about the process of AR modelling for roller element bearing signatures and the relation of the coefficients to the vibratory behavior of the bearings and their condition.

Fingerprint

pattern recognition
coefficients
time series analysis
rollers
signatures
methodology
vibration

Keywords

  • bearings (machine parts)
  • damage detection
  • failure analysis
  • pattern recognition
  • roller bearings
  • time series analysis
  • vibration analysis

Cite this

@article{1a00411cd19c4a348f3a4c64d1faebca,
title = "Autoregressive modelling for rolling element bearing fault diagnosis",
abstract = "In this study, time series analysis and pattern recognition analysis are used effectively for the purposes of rolling bearing fault diagnosis. The main part of the suggested methodology is the autoregressive (AR) modelling of the measured vibration signals. This study suggests the use of a linear AR model applied to the signals after they are stationarized. The obtained coefficients of the AR model are further used to form pattern vectors which are in turn subjected to pattern recognition for differentiating among different faults and different fault sizes. This study explores the behavior of the AR coefficients and their changes with the introduction and the growth of different faults. The idea is to gain more understanding about the process of AR modelling for roller element bearing signatures and the relation of the coefficients to the vibratory behavior of the bearings and their condition.",
keywords = "bearings (machine parts), damage detection, failure analysis, pattern recognition, roller bearings, time series analysis, vibration analysis",
author = "H Al-Bugharbee and I Trendafilova",
year = "2015",
month = "7",
day = "9",
doi = "10.1088/1742-6596/628/1/012088",
language = "English",
volume = "628",
journal = "Journal of Physics: Conference Series",
issn = "1742-6588",
number = "1",

}

Autoregressive modelling for rolling element bearing fault diagnosis. / Al-Bugharbee, H; Trendafilova, I.

In: Journal of Physics: Conference Series , Vol. 628, No. 1, 012088, 09.07.2015.

Research output: Contribution to journalConference Contribution

TY - JOUR

T1 - Autoregressive modelling for rolling element bearing fault diagnosis

AU - Al-Bugharbee, H

AU - Trendafilova, I

PY - 2015/7/9

Y1 - 2015/7/9

N2 - In this study, time series analysis and pattern recognition analysis are used effectively for the purposes of rolling bearing fault diagnosis. The main part of the suggested methodology is the autoregressive (AR) modelling of the measured vibration signals. This study suggests the use of a linear AR model applied to the signals after they are stationarized. The obtained coefficients of the AR model are further used to form pattern vectors which are in turn subjected to pattern recognition for differentiating among different faults and different fault sizes. This study explores the behavior of the AR coefficients and their changes with the introduction and the growth of different faults. The idea is to gain more understanding about the process of AR modelling for roller element bearing signatures and the relation of the coefficients to the vibratory behavior of the bearings and their condition.

AB - In this study, time series analysis and pattern recognition analysis are used effectively for the purposes of rolling bearing fault diagnosis. The main part of the suggested methodology is the autoregressive (AR) modelling of the measured vibration signals. This study suggests the use of a linear AR model applied to the signals after they are stationarized. The obtained coefficients of the AR model are further used to form pattern vectors which are in turn subjected to pattern recognition for differentiating among different faults and different fault sizes. This study explores the behavior of the AR coefficients and their changes with the introduction and the growth of different faults. The idea is to gain more understanding about the process of AR modelling for roller element bearing signatures and the relation of the coefficients to the vibratory behavior of the bearings and their condition.

KW - bearings (machine parts)

KW - damage detection

KW - failure analysis

KW - pattern recognition

KW - roller bearings

KW - time series analysis

KW - vibration analysis

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UR - http://iopscience.iop.org/1742-6596

U2 - 10.1088/1742-6596/628/1/012088

DO - 10.1088/1742-6596/628/1/012088

M3 - Conference Contribution

VL - 628

JO - Journal of Physics: Conference Series

T2 - Journal of Physics: Conference Series

JF - Journal of Physics: Conference Series

SN - 1742-6588

IS - 1

M1 - 012088

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