【作者】 陈涛；

【导师】 苏义鑫；

【作者基本信息】 武汉理工大学 ， 控制科学与工程， 2014， 硕士

【摘要】 船舶电站是整个船舶系统的动力心脏，为整个船舶系统提供原动力。随着现代船舶系统的日趋高性能化和结构复杂化，船舶电站的柴油发电机组也日益朝着大型化、高速化、精密化的方向发展，工作性能不断改善，自动化程度越来越高。一方面它将大大提高劳动生产率，提高船舶系统的电能质量，降低维护成本和能耗。但另一方面带来的问题是，一旦其中某一部分或某一环节发生故障，往往会使整个船舶系统处于瘫痪状态，直接或间接造成巨大的经济损失，甚至造成关键设备的损坏，危及人身安全。因此，如何迅速判断故障发生的原因，进而有效地排除故障，保证船舶的继续正常航行具有特别重要的意义。本文在分析国内外智能诊断技术研究现状及柴油机故障诊断诸多方法的基础上，给出了一种基于遗传神经网络的柴油机故障智能诊断方法，并且对柴油机燃油系统故障诊断问题进行了深入研究。首先，论文对故障诊断技术的研究现状及课题的研究内容进行了简要的论述，介绍了柴油机故障诊断的几种常见方法，并分析了神经网络故障诊断方法存在的问题。其次，针对BP神经网络收敛速度慢和容易陷入局部极小值问题，将遗传算法和神经网络有机结合，利用遗传算法的全局搜索能力，优化网络的初始权值和阀值，解决其本身固有的两个缺陷，进而提高了BP神经网络诊断故障时的准确性和快速性。最后，在MATLAB环境下进行仿真研究，把柴油机燃油系统典型故障数据作为遗传神经网络的训练样本，构建及训练网络，并对模拟故障进行诊断分析。仿真结果表明，基于遗传神经网络的故障诊断结果与实测值具有良好的一致性，只要选择足够典型的原始故障样本训练遗传神经网络，网络的稳定性就较好。基于遗传神经网络的故障模式识别方法能充分利用信息特征，实现输入与输出之间的映射关系，得出准确的诊断结果。更多还原

【Abstract】 The Marine power station is motive heart of the whole ships system, andwhich supplies power for the system. Gradually with the high performance and thestructure complication complex of the modern ship system, the diesel generator setwhich constitutes marine power station also increasingly develops toward large andhigh speed and precision. With the work performance continuously improving, thedegree of automating is higher. On the one hand it will rise to the rate of production,rise the electric power quantity of the ships system, lower the maintaining cost andenergy consuming; But on the other hand, the problem brought is that once some partor some a link among them go wrong, which usually will make the whole shipssystem in paralyze, so it directly or indirectly results in huge economy loss, evenresults in the key equipments damage, and endangers people safety. Therefore, how toquickly judge the reason of the fault and availably eliminate the faults, and guaranteethe ships continuing the normal voyage has special important meanings.Based on the analysis of domestic and foreign research about intelligentdiagnosis technology and fault diagnosis methods of diesel engine, a method of dieselengine fault diagnosis based on genetic neural network is given in this paper. Thefault diagnosis of fuel system is deeply studied.Firstly, the research status of fault diagnosis technology and contents of the topichave been discussed briefly in this paper; several popular methods of diesel enginefault diagnosis are introduced; the shortcoming of neural network in fault diagnosis isanalysized.Secondly，for shortcomings of slow convergence rate and falling into localminimum easily of BP neural network, genetic algorithm and neural network arecombined. Using the global search ability of genetic algorithm, initial weights andthresholds of neural network are optimized and the two inherent shortcomings ofnetwork are solved, which has enhanced the accuracy and rapidity of neural networkin fault diagnosis.Lastly, simulation test is carried on by the MATLAB, the network is constructedand trained using typical fault data of fuel system as genetic neural network training samples and simulation fault is diagnosed and analysized. The simulation resultsshow that the fault diagnosis result based on genetic neural network is well consistentwith measured values. As long as we choose enough typical initial fault samples totrain genetic neural network, the stability of network is better. The method of faultpattern recognition based on genetic neural network can fully use informationcharacteristics, achieve mapping relation between input and output and get accurateresult.更多还原