基于改进BP网络的柴油机燃油故障诊断的应用  

Application on the fault diagnosis of diesel engine fuel based on improved BP network

作　　者：顾秀江[1] 姚竹亭[1] 王洁[1] 刘勇锋[1] 秦新红[1] 

机构地区：[1]中北大学机械工程与自动化学院,山西太原030051

出　　处：《电子测试》2011年第8期4-7,共4页Electronic Test

基　　金：山西省自然科学基金资助(2010011031-1)

摘　　要：针对标准BP神经网络收敛速度较慢的问题,本文对所建立的BP网络的学习算法进行了改进,采用LM最优化算法对BP网络进行训练,替代了原来标准BP算法中的梯度下降法寻找最优网络连接权值。燃油喷射系统是柴油机的核心部分,其工作状况直接影响柴油机的燃油过程及其性能,将这种改进的BP算法应用到柴油机燃油故障诊断中,仿真实验证明,该学习算法提高了BP神经网络的学习效率及稳定性,明显地加快了网络的收敛速度,更好地实现了对燃油压力信号的故障诊断。Aiming at the problem of slower convergence rate of the standard BP neural network,in this paper the learning algorithm of established network is improved by LM optimization algorithm which replaces the gradient descent method of the standard BP algorithm to search for the optimal network connection weights.The fuel injection system is the core part of diesel engine,whose working condition directly affect the fuel process and performance of diesel engine.The improved BP algorithm is used in the fault diagnosis of diesel engine fuel.Simulation experiment has proved that this learning algorithm improves the learning efficiency and stability of BP network and obviously improves the convergence speed of the network,and better realizes the fault diagnosis of the fuel pressure signal.

关 键 词：BP网络 LM优化算法 故障诊断 

分 类 号：TK428[动力工程及工程热物理—动力机械及工程] TP183[自动化与计算机技术—控制理论与控制工程]