On-line Oil Monitoring and Diagnosis

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Izvoz citacije: ABNT
SALGUEIRO, José ;PERŠIN, Gabrijel ;VIŽINTIN, Jože ;IVANOVIČ, Matic ;DOLENC, Boštjan .
On-line Oil Monitoring and Diagnosis. 
Strojniški vestnik - Journal of Mechanical Engineering, [S.l.], v. 59, n.10, p. 604-612, june 2018. 
ISSN 0039-2480.
Available at: <https://www.sv-jme.eu/sl/article/on-line-oil-monitoring-and-diagnosis/>. Date accessed: 05 jul. 2020. 
doi:http://dx.doi.org/10.5545/sv-jme.2013.973.
Salgueiro, J., Peršin, G., Vižintin, J., Ivanovič, M., & Dolenc, B.
(2013).
On-line Oil Monitoring and Diagnosis.
Strojniški vestnik - Journal of Mechanical Engineering, 59(10), 604-612.
doi:http://dx.doi.org/10.5545/sv-jme.2013.973
@article{sv-jmesv-jme.2013.973,
	author = {José  Salgueiro and Gabrijel  Peršin and Jože  Vižintin and Matic  Ivanovič and Boštjan  Dolenc},
	title = {On-line Oil Monitoring and Diagnosis},
	journal = {Strojniški vestnik - Journal of Mechanical Engineering},
	volume = {59},
	number = {10},
	year = {2013},
	keywords = {predictive maintenance, oil condition monitoring, on-line oil analysis},
	abstract = {In condition monitoring (CM) of mechanical drives, the analysis of various physical and chemical properties of the operating lubricant can be used to diagnose defects and assess the state of the system. Recent developments in on-line oil condition sensors and advances in signal processing methods have allowed for a system for on-line oil analysis to be developed and applied in the field of predictive maintenance. The System for On-line Oil Analysis (SOOA) has the ability to measure multiple oil properties of interest and detect faults induced by transients in the acquired signals. Transient detection is based on the cumulative sum of errors (CUSUM) technique, where the error represents the difference between the predicted reference value and the current measured value. Detection of abnormal behaviour, based on transient detection, is followed by fault diagnosis, through integrated assessment of oil properties in real time. The system can operate as a standalone unit with an independent user interface or as a part of a complete integrated diagnostic system, merging oil condition evaluation with vibrational analysis and other techniques. This paper focuses on the algorithms within SOOA in charge of transient detection and fault diagnosis. The results of SOOA operation are presented through a demonstration of the method in a laboratory environment with two different sets of tests: gear pitting and water contamination.},
	issn = {0039-2480},	pages = {604-612},	doi = {10.5545/sv-jme.2013.973},
	url = {https://www.sv-jme.eu/sl/article/on-line-oil-monitoring-and-diagnosis/}
}
Salgueiro, J.,Peršin, G.,Vižintin, J.,Ivanovič, M.,Dolenc, B.
2013 June 59. On-line Oil Monitoring and Diagnosis. Strojniški vestnik - Journal of Mechanical Engineering. [Online] 59:10
%A Salgueiro, José 
%A Peršin, Gabrijel 
%A Vižintin, Jože 
%A Ivanovič, Matic 
%A Dolenc, Boštjan 
%D 2013
%T On-line Oil Monitoring and Diagnosis
%B 2013
%9 predictive maintenance, oil condition monitoring, on-line oil analysis
%! On-line Oil Monitoring and Diagnosis
%K predictive maintenance, oil condition monitoring, on-line oil analysis
%X In condition monitoring (CM) of mechanical drives, the analysis of various physical and chemical properties of the operating lubricant can be used to diagnose defects and assess the state of the system. Recent developments in on-line oil condition sensors and advances in signal processing methods have allowed for a system for on-line oil analysis to be developed and applied in the field of predictive maintenance. The System for On-line Oil Analysis (SOOA) has the ability to measure multiple oil properties of interest and detect faults induced by transients in the acquired signals. Transient detection is based on the cumulative sum of errors (CUSUM) technique, where the error represents the difference between the predicted reference value and the current measured value. Detection of abnormal behaviour, based on transient detection, is followed by fault diagnosis, through integrated assessment of oil properties in real time. The system can operate as a standalone unit with an independent user interface or as a part of a complete integrated diagnostic system, merging oil condition evaluation with vibrational analysis and other techniques. This paper focuses on the algorithms within SOOA in charge of transient detection and fault diagnosis. The results of SOOA operation are presented through a demonstration of the method in a laboratory environment with two different sets of tests: gear pitting and water contamination.
%U https://www.sv-jme.eu/sl/article/on-line-oil-monitoring-and-diagnosis/
%0 Journal Article
%R 10.5545/sv-jme.2013.973
%& 604
%P 9
%J Strojniški vestnik - Journal of Mechanical Engineering
%V 59
%N 10
%@ 0039-2480
%8 2018-06-28
%7 2018-06-28
Salgueiro, José, Gabrijel  Peršin, Jože  Vižintin, Matic  Ivanovič, & Boštjan  Dolenc.
"On-line Oil Monitoring and Diagnosis." Strojniški vestnik - Journal of Mechanical Engineering [Online], 59.10 (2013): 604-612. Web.  05 Jul. 2020
TY  - JOUR
AU  - Salgueiro, José 
AU  - Peršin, Gabrijel 
AU  - Vižintin, Jože 
AU  - Ivanovič, Matic 
AU  - Dolenc, Boštjan 
PY  - 2013
TI  - On-line Oil Monitoring and Diagnosis
JF  - Strojniški vestnik - Journal of Mechanical Engineering
DO  - 10.5545/sv-jme.2013.973
KW  - predictive maintenance, oil condition monitoring, on-line oil analysis
N2  - In condition monitoring (CM) of mechanical drives, the analysis of various physical and chemical properties of the operating lubricant can be used to diagnose defects and assess the state of the system. Recent developments in on-line oil condition sensors and advances in signal processing methods have allowed for a system for on-line oil analysis to be developed and applied in the field of predictive maintenance. The System for On-line Oil Analysis (SOOA) has the ability to measure multiple oil properties of interest and detect faults induced by transients in the acquired signals. Transient detection is based on the cumulative sum of errors (CUSUM) technique, where the error represents the difference between the predicted reference value and the current measured value. Detection of abnormal behaviour, based on transient detection, is followed by fault diagnosis, through integrated assessment of oil properties in real time. The system can operate as a standalone unit with an independent user interface or as a part of a complete integrated diagnostic system, merging oil condition evaluation with vibrational analysis and other techniques. This paper focuses on the algorithms within SOOA in charge of transient detection and fault diagnosis. The results of SOOA operation are presented through a demonstration of the method in a laboratory environment with two different sets of tests: gear pitting and water contamination.
UR  - https://www.sv-jme.eu/sl/article/on-line-oil-monitoring-and-diagnosis/
@article{{sv-jme}{sv-jme.2013.973},
	author = {Salgueiro, J., Peršin, G., Vižintin, J., Ivanovič, M., Dolenc, B.},
	title = {On-line Oil Monitoring and Diagnosis},
	journal = {Strojniški vestnik - Journal of Mechanical Engineering},
	volume = {59},
	number = {10},
	year = {2013},
	doi = {10.5545/sv-jme.2013.973},
	url = {https://www.sv-jme.eu/sl/article/on-line-oil-monitoring-and-diagnosis/}
}
TY  - JOUR
AU  - Salgueiro, José 
AU  - Peršin, Gabrijel 
AU  - Vižintin, Jože 
AU  - Ivanovič, Matic 
AU  - Dolenc, Boštjan 
PY  - 2018/06/28
TI  - On-line Oil Monitoring and Diagnosis
JF  - Strojniški vestnik - Journal of Mechanical Engineering; Vol 59, No 10 (2013): Strojniški vestnik - Journal of Mechanical Engineering
DO  - 10.5545/sv-jme.2013.973
KW  - predictive maintenance, oil condition monitoring, on-line oil analysis
N2  - In condition monitoring (CM) of mechanical drives, the analysis of various physical and chemical properties of the operating lubricant can be used to diagnose defects and assess the state of the system. Recent developments in on-line oil condition sensors and advances in signal processing methods have allowed for a system for on-line oil analysis to be developed and applied in the field of predictive maintenance. The System for On-line Oil Analysis (SOOA) has the ability to measure multiple oil properties of interest and detect faults induced by transients in the acquired signals. Transient detection is based on the cumulative sum of errors (CUSUM) technique, where the error represents the difference between the predicted reference value and the current measured value. Detection of abnormal behaviour, based on transient detection, is followed by fault diagnosis, through integrated assessment of oil properties in real time. The system can operate as a standalone unit with an independent user interface or as a part of a complete integrated diagnostic system, merging oil condition evaluation with vibrational analysis and other techniques. This paper focuses on the algorithms within SOOA in charge of transient detection and fault diagnosis. The results of SOOA operation are presented through a demonstration of the method in a laboratory environment with two different sets of tests: gear pitting and water contamination.
UR  - https://www.sv-jme.eu/sl/article/on-line-oil-monitoring-and-diagnosis/
Salgueiro, José, Peršin, Gabrijel, Vižintin, Jože, Ivanovič, Matic, AND Dolenc, Boštjan.
"On-line Oil Monitoring and Diagnosis" Strojniški vestnik - Journal of Mechanical Engineering [Online], Volume 59 Number 10 (28 June 2018)

Avtorji

Inštitucije

  • University of Ljubljana, Faculty of Mechanical Engineering, Laboratory for Tribology and Interface Nanotechnology, Slovenia 1
  • Cranfield University, School of Engineering, Department of Engineering Computing and Cybernetics, UK 2
  • Institute Jožef Stefan, Department of Systems and Control, Slovenia 3
  • University of Ljubljana, Faculty of Electrical Engineering, Slovenia 4

Informacije o papirju

Strojniški vestnik - Journal of Mechanical Engineering 59(2013)10, 604-612

https://doi.org/10.5545/sv-jme.2013.973

In condition monitoring (CM) of mechanical drives, the analysis of various physical and chemical properties of the operating lubricant can be used to diagnose defects and assess the state of the system. Recent developments in on-line oil condition sensors and advances in signal processing methods have allowed for a system for on-line oil analysis to be developed and applied in the field of predictive maintenance. The System for On-line Oil Analysis (SOOA) has the ability to measure multiple oil properties of interest and detect faults induced by transients in the acquired signals. Transient detection is based on the cumulative sum of errors (CUSUM) technique, where the error represents the difference between the predicted reference value and the current measured value. Detection of abnormal behaviour, based on transient detection, is followed by fault diagnosis, through integrated assessment of oil properties in real time. The system can operate as a standalone unit with an independent user interface or as a part of a complete integrated diagnostic system, merging oil condition evaluation with vibrational analysis and other techniques. This paper focuses on the algorithms within SOOA in charge of transient detection and fault diagnosis. The results of SOOA operation are presented through a demonstration of the method in a laboratory environment with two different sets of tests: gear pitting and water contamination.

predictive maintenance, oil condition monitoring, on-line oil analysis