University of Hertfordshire

Diagnosis and prognosis of slow speed bearing behavior under grease starvation condition

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Diagnosis and prognosis of slow speed bearing behavior under grease starvation condition. / Elforjani, Mohamed.

In: Structural Health Monitoring, 28.04.2017.

Research output: Contribution to journalArticlepeer-review

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@article{fc5b734f81564fc690e5e6ea5784010d,
title = "Diagnosis and prognosis of slow speed bearing behavior under grease starvation condition",
abstract = "The monitoring and diagnosis of rolling element bearings with acoustic emission and vibration measurements has evolved as one of the much used techniques for condition monitoring and diagnosis of rotating machinery. Furthermore, recent developments indicate the drive toward integration of diagnosis and prognosis algorithms in future integrated machine health management systems. With this in mind, this article is an experimental study of slow speed bearings in a starved lubricated contact. It investigates the influence of grease starvation conditions on detection and monitoring natural defect initiation and propagation using acoustic emission approach. The experiments are also aimed at a comparison of results acquired by acoustic emission and vibration diagnosis on full-scale axial bearing. In addition to this, the article concentrates on the estimation of the remaining useful life for bearings while in operation. To implement this, a multilayer artificial neural network model has been proposed to correlate the selected acoustic emission features with corresponding bearing wear throughout laboratory experiments. Experiments confirm that the obtained results were promising and selecting this appropriate signal processing technique can significantly affect the defect identification.",
keywords = "Acoustic Emission, Vibration Measurements, Condition Monitoring, Remaining Useful Life, Slow Speed Bearings, Artificial Neural Network.",
author = "Mohamed Elforjani",
note = "This document is the Accepted Manuscript version. The final, definitive version of this paper has been published in Structural Health Monitoring, April 2017, DOI: https://doi.org/10.1177/1475921717704620, published by SAGE Publishing, All rights reserved.",
year = "2017",
month = apr,
day = "28",
doi = "10.1177/1475921717704620",
language = "English",
journal = "Structural Health Monitoring",
issn = "1475-9217",
publisher = "Sage",

}

RIS

TY - JOUR

T1 - Diagnosis and prognosis of slow speed bearing behavior under grease starvation condition

AU - Elforjani, Mohamed

N1 - This document is the Accepted Manuscript version. The final, definitive version of this paper has been published in Structural Health Monitoring, April 2017, DOI: https://doi.org/10.1177/1475921717704620, published by SAGE Publishing, All rights reserved.

PY - 2017/4/28

Y1 - 2017/4/28

N2 - The monitoring and diagnosis of rolling element bearings with acoustic emission and vibration measurements has evolved as one of the much used techniques for condition monitoring and diagnosis of rotating machinery. Furthermore, recent developments indicate the drive toward integration of diagnosis and prognosis algorithms in future integrated machine health management systems. With this in mind, this article is an experimental study of slow speed bearings in a starved lubricated contact. It investigates the influence of grease starvation conditions on detection and monitoring natural defect initiation and propagation using acoustic emission approach. The experiments are also aimed at a comparison of results acquired by acoustic emission and vibration diagnosis on full-scale axial bearing. In addition to this, the article concentrates on the estimation of the remaining useful life for bearings while in operation. To implement this, a multilayer artificial neural network model has been proposed to correlate the selected acoustic emission features with corresponding bearing wear throughout laboratory experiments. Experiments confirm that the obtained results were promising and selecting this appropriate signal processing technique can significantly affect the defect identification.

AB - The monitoring and diagnosis of rolling element bearings with acoustic emission and vibration measurements has evolved as one of the much used techniques for condition monitoring and diagnosis of rotating machinery. Furthermore, recent developments indicate the drive toward integration of diagnosis and prognosis algorithms in future integrated machine health management systems. With this in mind, this article is an experimental study of slow speed bearings in a starved lubricated contact. It investigates the influence of grease starvation conditions on detection and monitoring natural defect initiation and propagation using acoustic emission approach. The experiments are also aimed at a comparison of results acquired by acoustic emission and vibration diagnosis on full-scale axial bearing. In addition to this, the article concentrates on the estimation of the remaining useful life for bearings while in operation. To implement this, a multilayer artificial neural network model has been proposed to correlate the selected acoustic emission features with corresponding bearing wear throughout laboratory experiments. Experiments confirm that the obtained results were promising and selecting this appropriate signal processing technique can significantly affect the defect identification.

KW - Acoustic Emission, Vibration Measurements, Condition Monitoring, Remaining Useful Life, Slow Speed Bearings, Artificial Neural Network.

UR - http://journals.sagepub.com/doi/abs/10.1177/1475921717704620?journalCode=shma

U2 - 10.1177/1475921717704620

DO - 10.1177/1475921717704620

M3 - Article

JO - Structural Health Monitoring

JF - Structural Health Monitoring

SN - 1475-9217

ER -