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According to principles of aero-engine fault diagnosis and flight data,this paper introduces a fault diagnosis method based on reformative ART1 neural network.After data pretreatment,it uses four-circumscription and quadratic-estimation to build fault codes and eliminate inveracious fault,and also ameliorate input layer of ART1 network.So this network can diagnoses not only gusty fault,but also gradual fault.It is proved that this method has high precision,strong restrain against noises,and low rate of inveracious alarms. Foreword With in-depth development in comprehensive integrated control of flight-propeller system for modern airplane, especially battle plane, flight data play a bigger role than ever. By availing and making full use of information on operating condition of airplane reflected by flight data, potential airplane fault can be discovered and removed in early stage with favorable safety reliability and redundancy for longer lifecycle of airplane and aero-engine on board. However, due to impact from external foul flight environment, aging of relevant sensors, as well as causes such as random noise interference in fight data recording system and etc, certain error exists with flight data. To deal with it, a set of fault code extracting method is designed herein, and modified ART1 neural network is applied for fault diagnosis. ART1 neural network is highly effective for pinpoint and real-time diagnosis of accidental fault with aero-engine, together with foresighted forecast function for progressive fault.

Application of Modified ART1 Neural Network in Aero-engine Fault Diagnosis