Data mining and knowledge reuse for the initial systems design and manufacturing: Aero-engine service risk drivers

Service providers of civil aero engines are typically confronted with a high cost of maintenance, replacement and refurbishment of the service damaged components. In such context, service experience becomes a key issue for determining the service risk drivers for operational disruptions and maintenance burden. This paper presents an industrial case study to produce new knowledge on the relationships between degradation and component design to manufacture. The study applied semantic data mining as a methodology for an efficient and the consistent data capture, representation, and analysis. The paper aims at identifying the service risk drivers based on service experience and event data. The analysis shows that the 3 top mechanisms accounting for 32% of the mechanism references have a strong Pareto effect. The paper concludes with missing information links and future research directions.