Predictive diagnosis based on a fleet-wide ontology approach
Résumé
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, subsystems and components of different technologies, different usages, different ages, etc. 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 thusnecessary 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 pointsof similarity. Hence, it allows to reuse past feedback experiences to build fleet-wide statistics and to search "deeper" causes producing an operation drift.