Predictive diagnosis based on a fleet-wide ontology approach

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.