[1]刘永建,朱剑英,曾捷,等.改进BP神经网络在发动机性能趋势分析和故障诊断中的应用[J].南京理工大学学报(自然科学版),2010,(01):24-29.
　LIU Yong-jian,ZHU Jian-ying,ZENG Jie.Improved BP Neural Network System for Engine Performance Trend Analysis and Fault Diagnosis[J].Journal of Nanjing University of Science and Technology,2010,(01):24-29.
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改进BP神经网络在发动机性能趋势分析和故障诊断中的应用
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《南京理工大学学报》（自然科学版）[ISSN:1005-9830/CN:32-1397/N]

卷:
期数:
2010年01期
页码:
24-29
栏目:
出版日期:
2010-02-28

文章信息/Info

Title:
Improved BP Neural Network System for Engine Performance Trend Analysis and Fault Diagnosis
作者:
刘永建;朱剑英;曾捷;
南京航空航天大学民航学院
Author(s):
LIU Yong-jian1; ZHU Jian-ying1; ZENG Jie2
1.College of Civil Aviation;2.College of Aerospace Engineering,Nanjing University ofAeronautics and Astronautics,Nanjing 210016,China
关键词:
神经网络;  蚁群优化算法;  Levenberg-Marquardt算法;  航空发动机;  性能趋势分析;  故障诊断
Keywords:
neural network;  ant colony optimization algorithm;  Levenberg-Marquardt algorithm;  aeroengines;  performance trend analysis;  fault diagnosis
分类号:
V263.6;TP183
摘要:
针对常规BP神经网络参数的经验式取值方法以及收敛速度慢,容易陷入局部最小点等缺陷,设计了一种改进的神经网络系统,利用蚁群算法优化神经网络连接权初值,并采用LM算法对人工神经网络进行训练,提高了网络的收敛速度,降低了训练误差。将其应用于某型利用ACARS报文实时获取飞机性能参数的发动机趋势分析和故障诊断中,可以快速准确地实现对发动机的性能趋势分析和复杂故障的诊断。最后通过仿真,对算法进行检验,结果表明改进算法的诊断置信度比改进前高。
Abstract:
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.

参考文献/References:

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备注/Memo

备注/Memo:
作者简介: 刘永建( 1972- ), 男, 博士生, 主要研究方向: 远程实时故障诊断、决策, E-m a il: ly j3924111@ sina. com;通讯作者: 朱剑英( 1937- ) , 男, 教授, 博士生导师, 主要研究方向: 人工智能与决策, E-m a il: zjyao@ nuaa.edu. cn。
更新日期/Last Update: 2012-11-02