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Improved BP Neural Network System for Engine Performance Trend Analysis and Fault Diagnosis


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Improved BP Neural Network System for Engine Performance Trend Analysis and Fault Diagnosis
LIU Yong-jian1ZHU Jian-ying1ZENG Jie2
1.College of Civil Aviation;2.College of Aerospace Engineering,Nanjing University ofAeronautics and Astronautics,Nanjing 210016,China
neural network ant colony optimization algorithm Levenberg-Marquardt algorithm aeroengines performance trend analysis fault diagnosis
Aiming at the disadvantage of conventional BP neural network,such as selecting parameter values by the empirical method,slow convergence speed and easy trap into local minimum points,this paper designs an improved BP neural network system.In order to improve the network convergence rate and reduce the training error,this paper optimizes the initial connecting weight value of neural network by the use of ant colony algorithm and trains artificial neural network by Levenberg-Marquardt(LM) algorithm.The algorithm is applied to an engine for access to the aircraft performance parameters of aeroengine trend analysis and fault diagnosis by using ACARS messages in real time.It can analyse the aeroengine performance trend and diagnose complex fault quickly and accurately.Finally,the improved algorithm is tested through simulation and the results show that the improved algorithm can get higher confidence level than the previous algorithm.


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Last Update: 2012-11-02