【作者】 李明钊；

【导师】 杨承志；

【作者基本信息】 昆明理工大学 ， 控制理论与控制工程， 2008， 硕士

【摘要】 随着汽车工业的迅速发展,汽车生产量和保有量不断增长,而且汽车制造技术的不断进步,使汽车发动机的越来越复杂。2008年将在全国实施机动车污染物排放国Ⅲ标准(相当于欧Ⅲ)标准,并强制要求安装车载诊断系统(简称OBD)。在现代化社会中,车用汽油机的故障诊断技术越来越受到重视,如果车用汽油机的某些部位出现故障而未能及时地发现和排除,其结果不仅会导致汽油机本身的损坏,甚至可能会造成车毁人亡的严重后果。近年来,我国汽车工业得到了迅速的发展,给人们生活带来了极大的方便,但是由于设备更复杂与数量更多给汽车维修人员带来了难题。因此研究车用汽油机的智能故障诊断技术具有实际意义。而且汽车的安全运行问题受到越来越多的关注,加强汽车的安全技术检测,成为有待研究解决的重要课题。在这样的背景下,本文针对传统故障诊断专家系统获取知识的瓶颈,不具备自学习的功能,采用人工神经网络和模糊理论来研究电控汽油机的智能故障诊断。针对电控汽油机的的怠速或怠速控制阀故障、点火线圈故障、点火正时不对、火花塞故障、节气门故障、进气管漏气、空气滤清器故障、喷油器故障、燃油供给系统故障、冷却系统故障及润滑系统故障,设计了BP诊断网络和模糊BP诊断网络。根据神经网络的特点,指出采用神经网络进行故障诊断的可行性。仿真结果表明对于电控汽油机的故障诊断而言,BP网络确实为一种较为实用的神经网络,它具有很强的模式识别和分类能力。由于电控汽油机故障的复杂性和模糊性,采用传统的以布尔代数为基础的二值逻辑显得过于粗糙不精确,因此引入模糊逻辑的概念,构造了模糊神经网络,并用它进行电控汽油机的故障诊断。仿真结果表明,将模糊逻辑引入神经网络后,对知识的表示更加准确,不仅对输入故障现象的描述更加细致,而且对输出故障的原因也有明确的解释,更符合人们的思维习惯。更多还原

【Abstract】 With the rapid development of the auto industry, auto production and keep growing volume, and automobile manufacturing technology continues to progress, so that the automobile engine become increasingly complex. the standards III of emissions of motor vehicle will be implemented nationwide(equivalent to Euro III) , and mandating the installation of on-board diagnostic systems (OBD) in 2008.In modern society,it is payed more and more attention to the automotive gasoline engine fault diagnosis technology, if the automotive gasoline engine occur failure in certain areas but not in time to discover and remove, and the results will not only lead to damage to gasoline engine itself, and it might even cause a fatal car crash of the serious consequences. In recent years, China’s auto industry has developed rapidly , so improve our lives, but because of more complex equipment and the number of vehicle become more , this is a problem to maintenance staff.It is practical significance to study the automotive gasoline engine intelligent fault diagnosis technology. And the safe operation of motor vehicles is more and more concerned,so enhance vehicle safety testing technology that would be important to study and solve the issue. Against such a backdrop, in this paper , traditional fault diagnosis expert system has knowledge acquisition bottleneck, and does not have the self-learning function, using artificial neural networks and fuzzy theory to study intelligent fault diagnosis electronic control gasoline engine. In allusion to idling or idling control valve failure, ignition coil failure , ignition timing is wrong, sparkplug fault ,throttle failure, leak into the trachea, the air filter failure, fault injector ,fuel supply system failure,cooling system failure, lubrication system failure of the electronic Control gasoline engine, I have designed BP diagnostic network and fuzzy BP diagnostic network. According to the characteristics of neural networks, indicated that neural network is feasible and inevitable for fault diagnosis. BP network is used for fault diagnosis for gasoline engine, simulation results show that BP network is a more practical neural network for fault diagnosis of electric control gasoline engine, its pattern recognition and classification is better. Because of complexity and ambiguity of diagnosis of electric control gasoline engine ,it is too rough and imprecise to adapt traditional two-valued logic based on Boolean algebra,therefore the concept of fuzzy logic is introduced, and fuzzy neural network is constructed, and it used to carry out fault diagnosis for electronic control gasoline engine. The simulation results show that fuzzy logic is used to neural networks, the expression of knowledge is more precise, it is not only more detailed description on the importation fault phenomenon, but also is a clear explanation for the reasons for the output fault, it is more in line with our thinking habits.更多还原