Abstract
Abstract Highly sophisticated control strategies are generally employed to control aircraft gas turbine engines which have to operate in harsh environmental conditions. Faults are difficult to detect with this increased complexity and the present fault detection systems sometimes indicate the wrong components to be faulty. These faults cost the airline industry millions of pounds each year. To improve the performance of these existing systems an autonomously learning fault detection system is proposed. The system is capable of detecting new faults and also adapting to faults that are similar to faults previously encountered. This paper presents the performance of this system when applied to real engine data and gives details on the improvements made to the system.
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