某弹药协调器液压系统的故障诊断方法研究

【作者】 张勇；

【导师】 侯保林；

【作者基本信息】 南京理工大学 ， 火炮、自动武器与弹药工程， 2017， 硕士

【摘要】 未来的火炮弹药装填系统将朝着自动化、信息化和智能化方向发展。弹药装填系统的目标就是要实现任意射角、任意方位角下的全自动弹药装填,实现工作状态的自动监测和故障诊断。协调器作为供弹装置的关键部件,其动作过程由液压系统驱动,而液压系统又是火炮故障频发的子系统之一,因其工作的封闭性,故障模式及其影响多样化,很难快速准确地定位故障并补救。所以,对协调器液压系统进行故障诊断研究至关重要。本文结合函数型主成分分析(FPCA)在特征提取过程以及神经网络在故障诊断方法中的特点,研究了一种将函数型主成分分析和BP神经网络相结合的故障诊断方法,并将其应用到协调器液压系统的故障诊断中。本文主要完成了以下工作:(1)分析了协调器及其液压系统的结构和工作原理,研究其故障机理与故障模式,总结了液压系统的共性故障和协调器液压系统的个性故障,同时建立了协调器液压系统的FMEA表格。(2)在ADAMS和AMESim中建立了协调器液压系统的联合仿真模型,确定了液压系统的故障检测信号,并结合实验数据进行对比。同时将联合仿真模型的仿真数据与MATLAB中液压模型的仿真数据相对比,进一步验证模型的正确性。(3)选取了协调器液压系统的典型故障参数,并进行仿真,为后续故障诊断提供故障数据。(4)利用函数型主成分分析对样本数据函数化后提取了特征参数,并将特征参数与故障参数之间的映射通过BP神经网络训练,验证了其可行性,最终完成故障诊断方法研究。更多还原

【Abstract】 The future of artillery ammunition loading system will move towards automation,informatization and intelligent direction.Ammunition loading system goal is to achieve any shot angle or arbitrary azimuth angle under the automatic ammunition loading,to achieve automatic monitoring of work status and fault diagnosis.As a key component of the ammunition device,the coordinator is driven by hydraulic system.Hydraulic system is one of the subsystems of the artillery failure.Because of the closeness and diversity of failure modes and effects,it is difficult to locate faults quickly and accurately and to remedy them.Therefore,it is very important to study the fault diagnosis of the hydraulic system of the coordinator.Combining the characteristics of Function principal component analysis(FPCA)in the feature extraction process and neural network in fault diagnosis,a method of fault diagnosis based on function principal component analysis and BP neural network is studied,which is applied to fault diagnosis of hydraulic system of coordinator.This paper mainly completed the following work:(1)This paper analyze the structure and working principle of the coordinator and its hydraulic system,study the failure mechanism and failure mode,sum up the common fault of the hydraulic system and the individual fault of the coordinator hydraulic system,and establish the FMEA form.(2)In the ADAMS and AMESim,the co-simulation model of the hydraulic system of the coordinator is established,and the failure state information of the hydraulic system is determined which is compared with the experimental data of the real equipment.Simultaneously,the simulation data of the co-simulation model are compared with the data of the hydraulic model in MATLAB to further verify the correctness of the model.(3)The typical fault parameters of the hydraulic system of the coordinator are selected and simulated,which can provide fault data for the fault diagnosis system.(4)Using function principal component analysis,the sample data are functionized and the feature parameters are extracted.The mapping between the characteristic parameters and the fault parameters is trained by BP neural network to verify its feasibility.Finally,the fault diagnosis method is completed.更多还原