The article addresses data-driven fault detection in commercial aircraft gas turbine engines in the framework of multisensor information fusion and symbolic dynamic filtering. The hierarchical decision and control structure, adopted in this article, involves construction of composite patterns, namely, atomic patterns extracted from single sensors, and relational patterns representing cross-dependence between a pair of sensors. While the underlying theories are presented along with necessary assumptions, the proposed method is validated on the NASA C-MAPSS simulation test bed of aircraft gas turbine engines; both single-fault and multiple-fault scenarios have been investigated. Since aircraft engines undergo natural degradation during the course of their normal operation, the issue of distinguishing between a fault and natural degradation is also addressed.
|Original language||English (US)|
|Number of pages||14|
|Journal||Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering|
|State||Published - Dec 2013|
All Science Journal Classification (ASJC) codes
- Aerospace Engineering
- Mechanical Engineering