柴油机缸套—活塞组磨损状态的识别研究

【作者】 袁东来；

【导师】 陈欠根；

【作者基本信息】 中南大学 ， 机械电子工程， 2007， 硕士

【摘要】 缸套-活塞组是柴油机的动力源,尽管该组件在柴油机中故障率并不是最高的,然而一旦它出现异常磨损,轻则造成整机性能变差,重则整机报废。因此,对柴油机缸套-活塞组进行状态监测,对于保持柴油机良好的工作状态具有重要意义。基于以上考虑,本文进行了柴油机缸套-活塞组工作状态识别的研究。首先,本文对机身振动信号和活塞与缸套之间撞击情形进行了分析,建立了二者之间的映射关系,并明确了柴油机机型、运行工况等因素对映射关系的影响;其次,考虑到柴油机机身振动信号是非平稳随机过程,本文主要利用了小波包时频分析方法对机身振动信号进行处理,绘制了小波包三维时频能量图,提出主撞击能量特征参数作为监测缸套-活塞组工作状态的重要依据;再次,本文将遗传算法与神经网络结合,改善了网络性能。在遗传算法初步优化权值的基础上,再进行BP网络的训练和识别,充分利用神经网络的非线性映射能力,实现了柴油机缸套-活塞组工作状态的识别;最后,对不同机型的缸内部件不同的磨损失效形式进行了实验,验证了本文提取特征参数的有效性。结果表明,振动分析法能在不拆机的前提下准确的识别出柴油机缸套-活塞组的工作状态。该方法简单可靠,具有良好的工程应用前景。更多还原

【Abstract】 Cylinder-piston assembly is the power source of a diesel engine. Though failure rate of the assembly isn’t the highest, once it appears abnormal wear, the operation quality of the diesel engine is decreased than before, or even the diesel engine is destroyed. Thus, monitoring the working state of cylinder-piston assembly is very important for keeping diesel engine in a good operation condition.Based on the reasons above, a research on the identification of working state of the cylinder-piston assembly is carried out in this thesis. Firstly, the thesis analyzes the vibration signal of diesel engine surface and the crashing process between piston and cylinder, establishes the mapping relationship between them, and masters the influence factors that is the diesel engine models, operating conditions and so on. Secondly, considering that the vibration signal is non-stationary stochastic process, the thesis mainly uses wavelet packets to deal with the vibration signal, plots the three-dimensional time-frequency energy chart of the wavelet packets, and proposes using characteristic parameter of the main impact energy as an important basis to monitor cylinder-piston assembly working state. Thirdly, the thesis combines genetic algorithm and neural network, which will improve the network performance. On the basis of primarily optimizing the weight distribution with genetic algorithm, the thesis trains and recognizes the BP network, realizes the identification of the cylinder-piston working state assembly by means of the nonlinear mapping ability of neural network. Finally, the thesis validates the effectiveness of extracting characteristic parameters by experiments taken on cylinder inner parts of different machine types and different failure forms.The result shows that vibration analysis method can accurately identify the working state of cylinder-piston assembly without unseam machine.The method, which is simple and reliable, shows us a good prospect in the engineering application.更多还原