【作者】 罗锋；

【导师】 李艳军；

【作者基本信息】 南京航空航天大学 ， 载运工具运用工程， 2009， 硕士

【摘要】 航空发动机是民航飞机的动力核心,因此对其运行状态进行监测至关重要。在磨损故障监测中,针对不同的监测对象,有选择地联合运用几种油液监测手段,是获得更为准确分析结论的有效途径。本文以XRF技术和显微图像技术为基础,引入神经网络、灰色系统理论、概率统计、D-S证据理论等智能方法,对航空发动机的智能监测技术进行研究,致力于研制出一套智能化、系统化的智能监测系统。XRF(X射线荧光)技术利用同位素放射源辐射被测物质,通过一系列装置产生能谱,根据能谱来判别物质的元素类别。显微图像技术通过显微光学成像系统、图像采集与分析处理等过程,实现对发动机磨损磨粒的监测,具有颗粒计数器与铁谱仪的双重功能。本文研究工作主要包括以下三部分:首先,在基于XRF分析技术得到能谱数据的基础上,对能谱进行处理和分析,得到元素的浓度;研究了金属元素浓度的界限值的制定方法,以及在此基础上的故障诊断,并运用灰色预测实现了发动机的故障预测。其次,在显微图像分析得到磨损磨粒参数以及磨粒数目的基础上,实现了运用径向基函数神经网络(RBF)、BP神经网络和灰度关联分析对磨损磨粒的融合识别,提高了磨粒识别的准确率。在磨粒识别的基础上实现了对发动机故障进行监测。最后,提出了基于以上两种分析的融合诊断方法以及智能综合分析模型,并开发了智能监测系统软件。更多还原

【Abstract】 Aeroengine supplies the major motive power to the modern aircraft, so the status inspection of the aero-engine operation plays undoubtedly an important role. According to different objects, the conclusion of fuseing several different monitoring means is more accurately. This paper beased on XRF (X-ray fluorescence) technology and introducing artificial neural network,gray system theory,probability statistics,Dempster-Shafer evidential theory and other intelligent calculation,in order to develop a intelligent monitoring system.XRF (X-ray fluorescence) technology, using isotope radioactive sources to eradiate materials which to be measured, produces energy spectrum through a series of installations, which is used to determine the elemental material categories. Microscopic image technique which with the functions of particle counter and ferrograph , achieved the diagnosis on the aero-engine wear faults by useing mcroscopic optical imaging system and digital image processing method.The research work mainly includes the following three aspects. Firstly, processed XRF energy spectrum and out putted the element concentrations. Researched the methord of calculating the threshold value of the concentration of the wear debris and forecasted the wear fault by grey prediction. Secondly, based on the number and parameters of wear debris, fusing the radial based function (RBF) neural network, back-propagation (BP) neural network and grey relational analysis methods, improving the accuracy rate of debris auto-recognition based on the collected information of the debris configuration characteristics; and conducting the diagnosis on the aero-engine wear faults according to the recognition.Finally, a synthetic intelligent diagnosis model by data fusion fault diagnosis method based on above diagnosis method was put forward, an intelligent monitoring software system of aero-engine is developed based on the above research.更多还原