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Data-driven imbalance and hard particle detection in rotating machinery using infrared thermal imaging

Olivier Janssens UGent, Mia Loccufier UGent, Rik Van de Walle UGent and Sofie Van Hoecke UGent (2017) INFRARED PHYSICS & TECHNOLOGY. 82. p.28-39
abstract
Currently, temperature-based condition monitoring cannot be used to accurately identify potential faults early in a rotating machines' lifetime since temperature changes are only detectable when the fault escalates. However, currently only point measurements, i.e. thermocouples, are used. In this article, infrared thermal imaging is used which - as opposed to simple thermocouples - provides spatial temperature information. This information proves crucial for the identification of several machine conditions and faults. In this paper the conditions considered are outer-raceway damage in bearings, hard-particle contamination in lubricant and several gradations of shaft imbalance. The fault detection is done using an image processing and machine learning solution which can accurately detect the majority of the faults and conditions in our data set. (C) 2017 Elsevier B.V. All rights reserved.
Please use this url to cite or link to this publication:
author
organization
year
type
journalArticle (original)
publication status
published
keyword
IBCN
journal title
INFRARED PHYSICS & TECHNOLOGY
volume
82
pages
28 - 39
Web of Science type
Article
Web of Science id
000400530100004
ISSN
1350-4495
1879-0275
DOI
10.1016/j.infrared.2017.02.009
language
English
UGent publication?
yes
classification
A1
id
8524171
handle
http://hdl.handle.net/1854/LU-8524171
date created
2017-06-19 08:32:37
date last changed
2017-06-21 13:28:09
@article{8524171,
  abstract     = {Currently, temperature-based condition monitoring cannot be used to accurately identify potential faults early in a rotating machines' lifetime since temperature changes are only detectable when the fault escalates. However, currently only point measurements, i.e. thermocouples, are used. In this article, infrared thermal imaging is used which - as opposed to simple thermocouples - provides spatial temperature information. This information proves crucial for the identification of several machine conditions and faults. In this paper the conditions considered are outer-raceway damage in bearings, hard-particle contamination in lubricant and several gradations of shaft imbalance. The fault detection is done using an image processing and machine learning solution which can accurately detect the majority of the faults and conditions in our data set. (C) 2017 Elsevier B.V. All rights reserved.},
  author       = {Janssens, Olivier and Loccufier, Mia and Van de Walle, Rik and Van Hoecke, Sofie},
  issn         = {1350-4495},
  journal      = {INFRARED PHYSICS \& TECHNOLOGY},
  keyword      = {IBCN},
  language     = {eng},
  pages        = {28--39},
  title        = {Data-driven imbalance and hard particle detection in rotating machinery using infrared thermal imaging},
  url          = {http://dx.doi.org/10.1016/j.infrared.2017.02.009},
  volume       = {82},
  year         = {2017},
}

Chicago
Janssens, Olivier, Mia Loccufier, Rik Van de Walle, and Sofie Van Hoecke. 2017. “Data-driven Imbalance and Hard Particle Detection in Rotating Machinery Using Infrared Thermal Imaging.” Infrared Physics & Technology 82: 28–39.
APA
Janssens, O., Loccufier, M., Van de Walle, R., & Van Hoecke, S. (2017). Data-driven imbalance and hard particle detection in rotating machinery using infrared thermal imaging. INFRARED PHYSICS & TECHNOLOGY, 82, 28–39.
Vancouver
1.
Janssens O, Loccufier M, Van de Walle R, Van Hoecke S. Data-driven imbalance and hard particle detection in rotating machinery using infrared thermal imaging. INFRARED PHYSICS & TECHNOLOGY. 2017;82:28–39.
MLA
Janssens, Olivier, Mia Loccufier, Rik Van de Walle, et al. “Data-driven Imbalance and Hard Particle Detection in Rotating Machinery Using Infrared Thermal Imaging.” INFRARED PHYSICS & TECHNOLOGY 82 (2017): 28–39. Print.