【作者】 刘良顺；

【导师】 宋希庚；

【作者基本信息】 大连理工大学 ， 动力机械及工程， 2005， 硕士

【摘要】 离心压缩机是大型石化企业中的关键生产设备,保障其安全运转十分重要。目前,多数企业都实现了压缩机组的实时状态监测,但是,故障诊断环节却相对薄弱。基于计算机的智能故障诊断是目前各国学者竞相研究的热点,各种先进的智能诊断理论都被应用到这一领域。尤其是近年来比较流行的人工神经网络技术,以其强大的函数逼近能力和模式识别能力,在非平稳时间序列预测和故障诊断领域得到了广泛的应用。 对反映机组状态的关键参数进行趋势预测,同时对机组可能出现的故障进行诊断,是机械设备状态监测与故障诊断系统中最重要的两部分内容。本文研究了基于RBF神经网络和Adaline神经网络的非平稳时间序列预测理论,并将其应用于离心压缩机转子振动状态的预测。此外,还研究了基于小波分析和人工神经网络的离心压缩机故障诊断技术。根据课题的需要,笔者参与研制了离心压缩机状态监测与故障诊断实验系统,编制了该系统的热力性能监测模块和故障诊断模块。结合理论研究和实验分析,编制了适用于信号处理、小波分析以及神经网络分析的通用模块化程序。 本文还对滚动轴承的早期表面损伤故障诊断技术进行了研究。滚动轴承是旋转机械中的重要部件,对其早期损伤进行故障诊断,实质上就是进行故障的预防。本文建立了滚动轴承单点损伤和多点损伤的共振解调法理论振动模型,并对单点损伤理论模型进行了验证,结果表明该模型能够对滚动轴承的早期损伤做出精确的诊断。除此之外,本文还研究了基于RBF神经网络的滚动轴承故障诊断技术,网络的实际仿真性能良好。更多还原

【Abstract】 Centrifugal compressor is one of the most important mechanisms in chemical plants, and it’s necessary for us to monitor its working conditions. By now, most of the chemical plants have equipped with online monitoring systems for centrifugal compressors, but few of those systems have fault diagnosis functions. Considerable attention has been devoted to the study of intelligent fault diagnosis for large rotating mechanisms in recent years, and all kinds of advanced intelligent diagnosis theories have been applied to this research. Especially the fashionable method of artificial neural networks, which has powerful abilities of function approximation and pattern recognition, has been widely used in fields of nonstationary time series forecasting and fault diagnosis.Forecasting parameters that reflecting the equipment state, and diagnosis for some probable faults are two most important parts ins ystem of state monitoring and fault diagnosis. In this paper, application of RBF(Radial Basis Function) neural network and Adaline(Adaptive Linear Element) neural network for nonstationary time series forecasting is discussed, and they have been successfully applied to the vibration forecasting of centrifugal compressor. On the other hand, fault diagnosis for centrifugal compressor based on wavelet transform and artificial neural network is also studied in this paper. An experiment system of state monitoring and fault diagnosis for centrifugal compressor is developed by our project group, and the mainly job for the author is to compile software modules, including thermal performance monitoring module and fault diagnosis module. A universal and modularized program is also developed based on theoretical research and experimental results, which is suitable for digital signal processing and wavelet transform and artificial neural networks analysis.Diagnostics for rolling element bearing is also investigated in this paper. Fault detection at the early stage of failure development can be seen as fault prediction to a certain extent. Vibration models of one-point defect and multi-points defects for rolling element bearings are established based on the method of demodulated resonance technique (DRT), and one-point defect vibration model is testified to be a so sensitive and reliable method that it could find the fault position exactly. In addition, fault diagnosis for rolling element bearing using RBF(Radial Basis Function) neural networks is also discussed, and simulation result of the network is very good.更多还原