Thermal imaging and vibration-based multisensor fault detection for rotating machinery
- Author
- Olivier Janssens (UGent) , Mia Loccufier (UGent) and Sofie Van Hoecke (UGent)
- Organization
- Abstract
- In order to minimize operation and maintenance costs and extend the lifetime of rotating machinery, damaging conditions and faults should be detected early and automatically. To enable this, sensor streams should continuously be monitored, processed, and interpreted. In recent years, infrared thermal imaging has gained attention for the said purpose. However, the detection capabilities of a system that uses infrared thermal imaging is limited by the modality captured by this single sensor, as is any single sensor-based system. Hence, within this paper a multisensor system is proposed that not only uses infrared thermal imaging data, but also vibration measurements for automatic condition and fault detection in rotating machinery. It is shown that by combining these two types of sensor data, several conditions/faults and combinations can be detected more accurately than when considering the sensor streams individually.
- Keywords
- DIAGNOSIS, THERMOGRAPHY, Fault detection, feature extraction, machine learning, preventive, maintenance
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-8594139
- MLA
- Janssens, Olivier, et al. “Thermal Imaging and Vibration-Based Multisensor Fault Detection for Rotating Machinery.” IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, vol. 15, no. 1, Ieee-inst Electrical Electronics Engineers Inc, 2019, pp. 434–44, doi:10.1109/TII.2018.2873175.
- APA
- Janssens, O., Loccufier, M., & Van Hoecke, S. (2019). Thermal imaging and vibration-based multisensor fault detection for rotating machinery. IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 15(1), 434–444. https://doi.org/10.1109/TII.2018.2873175
- Chicago author-date
- Janssens, Olivier, Mia Loccufier, and Sofie Van Hoecke. 2019. “Thermal Imaging and Vibration-Based Multisensor Fault Detection for Rotating Machinery.” IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 15 (1): 434–44. https://doi.org/10.1109/TII.2018.2873175.
- Chicago author-date (all authors)
- Janssens, Olivier, Mia Loccufier, and Sofie Van Hoecke. 2019. “Thermal Imaging and Vibration-Based Multisensor Fault Detection for Rotating Machinery.” IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 15 (1): 434–444. doi:10.1109/TII.2018.2873175.
- Vancouver
- 1.Janssens O, Loccufier M, Van Hoecke S. Thermal imaging and vibration-based multisensor fault detection for rotating machinery. IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS. 2019;15(1):434–44.
- IEEE
- [1]O. Janssens, M. Loccufier, and S. Van Hoecke, “Thermal imaging and vibration-based multisensor fault detection for rotating machinery,” IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, vol. 15, no. 1, pp. 434–444, 2019.
@article{8594139,
abstract = {{In order to minimize operation and maintenance costs and extend the lifetime of rotating machinery, damaging conditions and faults should be detected early and automatically. To enable this, sensor streams should continuously be monitored, processed, and interpreted. In recent years, infrared thermal imaging has gained attention for the said purpose. However, the detection capabilities of a system that uses infrared thermal imaging is limited by the modality captured by this single sensor, as is any single sensor-based system. Hence, within this paper a multisensor system is proposed that not only uses infrared thermal imaging data, but also vibration measurements for automatic condition and fault detection in rotating machinery. It is shown that by combining these two types of sensor data, several conditions/faults and combinations can be detected more accurately than when considering the sensor streams individually.}},
author = {{Janssens, Olivier and Loccufier, Mia and Van Hoecke, Sofie}},
issn = {{1551-3203}},
journal = {{IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS}},
keywords = {{DIAGNOSIS,THERMOGRAPHY,Fault detection,feature extraction,machine learning,preventive,maintenance}},
language = {{eng}},
number = {{1}},
pages = {{434--444}},
publisher = {{Ieee-inst Electrical Electronics Engineers Inc}},
title = {{Thermal imaging and vibration-based multisensor fault detection for rotating machinery}},
url = {{http://doi.org/10.1109/TII.2018.2873175}},
volume = {{15}},
year = {{2019}},
}
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