[1]巴寅亮,王书提,谢鑫.基于改进的BP神经网络的柴油发动机故障诊断[J].工业仪表与自动化装置,2015,(03):94-97.
　BA Yinliang,WANG Shuti,XIE Xin.Research of diesel engine fault based on improved BP neural network[J].Industrial Instrumentation & Automation,2015,(03):94-97.
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基于改进的BP神经网络的柴油发动机故障诊断(PDF)

《工业仪表与自动化装置》[ISSN:1000-0682/CN:61-1121/TH]

作者:: 巴寅亮1; 2; 王书提1; 2; 谢鑫1; 2; （1. 新疆农业大学机械交通学院；2. 新疆农业工程装备创新设计重点实验室，乌鲁木齐 830052）

Author(s):: BA Yinliang1; 2; WANG Shuti1; 2; XIE Xin1; 2; (1. College of Mechanic and Traffic, Xinjiang Agricultural University;2. Key Laboratory for Agricultural Engineering Equipment Innovative Design of Xinjiang, Urumqi 830052,China）

Keywords:: diesel engine; high pressure common rail; improved BP neural network; LM algorithm; electronic controlled system; fault diagnosis

摘要:: 柴油发动机采用高压共轨燃油喷射技术，提高了柴油机的综合性能，但高压共轨柴油机电控系统比较复杂，增大了柴油机故障诊断的难度性。介绍了BP神经网路及LM算法，并利用改进的BP神经网络对发动机电控系统故障进行诊断研究。以长城哈佛GW2.8TC发动机为实验对象，让发动机在怠速状态下，对发动机进行故障设置，利用金德KT600故障诊断仪采集发动机的故障数据流，运用改进的BP神经网络建立诊断模型，诊断结果表明改进的BP神经网络的收敛速度快，运用改进的BP网路诊断柴油机电控系统故障是行之有效的。

Abstract:: Diesel engine with high pressure common rail fuel injection technology, improves the comprehensive performance of diesel engine, but the high pressure common rail diesel engine electronic controlled system is more complex, increasing the difficulty of diesel engine fault diagnosis. Introducing the BP Neural Network and LM algorithm, and carrying on the research on fault diagnosis of engine electronic controlled system based on improved BP Neural Network. Taking the Great Wall Harvard GW2.8TC engine as the experimental object, keeping the engine at idle speed condition, setting up some ?fault assumption for the engine, collecting the failure data flow of the engine by kinder KT600 fault diagnosis instrument, using improved BP Neural Network to establish diagnosis model, the diagnosis results show that the convergence rate of improved BP Neural Network is quickly, it is effective to diagnose electronic controlled system fault of diesel engine by improved BP Neural Network.

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更新日期/Last Update: 1900-01-01