A Multiple Classifier System for Aircraft Engine Fault Diagnosis
نویسندگان
چکیده
Multiple classifier systems (MCS) are considered as one of the most significant advances in pattern classification in recent years. Numerous studies (both theoretical and empirical) have proved that MCS are effective in achieving improved classification performance for various application problems. Aircraft engine fault diagnosis plays a crucial rule in costeffective operation of aircraft engines. By accurately detecting and reliably diagnosing impending engine faults, aircraft engine fault diagnosis can help to increase engine on-wing time, reduce maintenance turnaround time, reduce aircraft life-cycle costs, and increase flight safety. However, designing a reliable aircraft engine fault diagnostic system is a challenging task, due to a number of characteristics of aircraft engines. These characteristics include the wide range of flight regime that aircraft engines are operated over and that engines experience normal wear that needs to be differentiated from faults. Motivated by a goal of achieving the highest possible performance of fault diagnosis, we introduce MCS to aircraft engine fault diagnosis. By designing a real-world MCS-based aircraft fault diagnostic system, we demonstrate that MCS is effective in improving the performance of aircraft engine fault diagnostic systems.
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