For the effective operation of the air power in the modern war, aircraft should be well-maintained by preventing a series of failures. However, the current maintenance system employed by ROKAF (Republic of Korea Air Force) does not fully utilize cumulative sequential failure data. In this paper, we apply sequential association rules to extract the failure patterns and forecast failure sequences of ROKAF aircrafts according to various combinations of aircraft types, location, mission and season. It is expected that our analysis can add value to the existing maintenance database. Also, our approach can improve the utilization of aircrafts by properly forecasting the future demand of aircraft spare parts.
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Artificial Intelligence