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: 20 dec. 2024. 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. 20 Dec. 2024
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)
Strojniški vestnik - Journal of Mechanical Engineering 59(2013)10, 604-612
© The Authors, CC-BY 4.0 Int. Change in copyright policy from 2022, Jan 1st.
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.