Predictive diagnosis based on a fleet-wide ontology approach. Medina-Oliva, G., Voisin, A., Monnin, M., & Leger, J. Knowledge-Based Systems, 68:40–57, September, 2014. ![Predictive diagnosis based on a fleet-wide ontology approach [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Diagnosis is a critical activity in the PHM domain (Prognostics and Health Management) due to its impact on the downtime and on the global performances of a system. This activity becomes complex when dealing with large systems such as power plants, ships, aircrafts, which are composed of multiple systems, sub-systems and components of different technologies, different usages, and different ages. In order to ease diagnosis activities, this paper proposes to use a fleet-wide approach based on ontologies in order to capitalize knowledge and data to help decision makers to identify the causes of abnormal operations. In that sense, taking advantage of a fleet dimension implies to provide managers and engineers more knowledge as well as relevant and synthetized information about the system behavior. In order to achieve PHM at a fleet level, it is thus necessary to manage relevant knowledge arising from both modeling and monitoring of the fleet. This paper presents a knowledge structuring scheme of fleets in the marine domain based on ontologies for diagnostic purposes. The semantic knowledge model formalized with an ontology allowed to retrieve data from a set of heterogeneous units through the identification of common and pertinent points of similarity. Hence, it allows to reuse past feedback experiences to build fleet-wide statistics and to search “deeper” causes producing an operation drift.
@article{medina-oliva_predictive_2014,
series = {Enhancing {Experience} {Reuse} and {Learning}},
title = {Predictive diagnosis based on a fleet-wide ontology approach},
volume = {68},
issn = {0950-7051},
url = {https://www.sciencedirect.com/science/article/pii/S0950705113004000},
doi = {10.1016/j.knosys.2013.12.020},
abstract = {Diagnosis is a critical activity in the PHM domain (Prognostics and Health Management) due to its impact on the downtime and on the global performances of a system. This activity becomes complex when dealing with large systems such as power plants, ships, aircrafts, which are composed of multiple systems, sub-systems and components of different technologies, different usages, and different ages. In order to ease diagnosis activities, this paper proposes to use a fleet-wide approach based on ontologies in order to capitalize knowledge and data to help decision makers to identify the causes of abnormal operations. In that sense, taking advantage of a fleet dimension implies to provide managers and engineers more knowledge as well as relevant and synthetized information about the system behavior. In order to achieve PHM at a fleet level, it is thus necessary to manage relevant knowledge arising from both modeling and monitoring of the fleet. This paper presents a knowledge structuring scheme of fleets in the marine domain based on ontologies for diagnostic purposes. The semantic knowledge model formalized with an ontology allowed to retrieve data from a set of heterogeneous units through the identification of common and pertinent points of similarity. Hence, it allows to reuse past feedback experiences to build fleet-wide statistics and to search “deeper” causes producing an operation drift.},
language = {en},
urldate = {2021-09-28},
journal = {Knowledge-Based Systems},
author = {Medina-Oliva, Gabriela and Voisin, Alexandre and Monnin, Maxime and Leger, Jean-Baptiste},
month = sep,
year = {2014},
keywords = {Diagnostic, Knowledge capitalization, Knowledge reuse, Maintenance, Ontologies},
pages = {40--57},
}
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In order to ease diagnosis activities, this paper proposes to use a fleet-wide approach based on ontologies in order to capitalize knowledge and data to help decision makers to identify the causes of abnormal operations. In that sense, taking advantage of a fleet dimension implies to provide managers and engineers more knowledge as well as relevant and synthetized information about the system behavior. In order to achieve PHM at a fleet level, it is thus necessary to manage relevant knowledge arising from both modeling and monitoring of the fleet. This paper presents a knowledge structuring scheme of fleets in the marine domain based on ontologies for diagnostic purposes. The semantic knowledge model formalized with an ontology allowed to retrieve data from a set of heterogeneous units through the identification of common and pertinent points of similarity. 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