Advanced search
2 files | 3.62 MB

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)
Author
Organization
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.
Keywords
IBCN

Downloads

  • (...).pdf
    • full text
    • |
    • UGent only
    • |
    • PDF
    • |
    • 1.70 MB
  • DS5 i.pdf
    • full text
    • |
    • open access
    • |
    • PDF
    • |
    • 1.93 MB

Citation

Please use this url to cite or link to this publication:

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.
@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},
}

Altmetric
View in Altmetric
Web of Science
Times cited: