基于LabVIEW的压缩机振动分析与故障诊断方法研究

发布时间：2018-02-03 20:46

本文关键词： LabVIEW 轴流压缩机数据采集特征提取故障诊断　出处：《内蒙古大学》2012年硕士论文　论文类型：学位论文

【摘要】：随着工业生产和科学技术的发展,机械设备的可靠性、可用性、可维修性与安全性的问题日益突出,从而促进了人们对大型机械设备故障机理及诊断技术的研究。目前对压缩机故障机理的研究主要从故障规律、故障征兆和故障模型等方面进行。由于大部分轴系故障都在振动信号上反映出来,因此,对轴系故障的研究总是以振动信号的分析为主。故本文针对大型机械轴流压缩机的工作原理及故障特性,通过机械产生的振动信号对故障信号进行识别和诊断。 LabVIEW是目前应用最广、发展最快、功能最强的图形化软件开发集成环境,被视为一个标准的数据采集和仪器控制软件,本文利用LabVIEW软件平台构建了数据采集系统,采集了振动信号,用傅里叶变换和小波变换从时域和频域两个尺度对振动信号进行分析和处理,通过对压缩机振动状态的监测,提取了振动信号的有效特征,并结合模式识别算法对振动信号进行了分类和识别,对故障信号做出诊断,从而对振动信号实现故障诊断方法的研究。实验表明该系统的识别诊断功能是有效可行的,为大型机械轴系振动的分析和诊断提供了一种思路。
[Abstract]:With the development of industrial production and science and technology, the problems of reliability, availability, maintainability and safety of mechanical equipment become more and more prominent. This has promoted the research of fault mechanism and diagnosis technology of large-scale mechanical equipment. At present, the research on fault mechanism of compressor is mainly from the fault law. Fault symptoms and fault models are carried out. Because most shafting faults are reflected on the vibration signal, so. The research of shaft system fault is always based on the analysis of vibration signal, so this paper aims at the working principle and fault characteristics of large mechanical axial flow compressor. Fault signals are identified and diagnosed by mechanical vibration signals. LabVIEW is the most widely used, the fastest growing, the most powerful graphical software development integration environment, is regarded as a standard data acquisition and instrument control software. In this paper, a data acquisition system is constructed by using LabVIEW software platform, and vibration signals are collected. The vibration signals are analyzed and processed by Fourier transform and wavelet transform from two scales: time domain and frequency domain. By monitoring the vibration state of the compressor, the effective characteristics of the vibration signal are extracted, and the vibration signal is classified and identified with the pattern recognition algorithm, and the fault signal is diagnosed. Thus, the method of fault diagnosis based on vibration signal is studied. The experimental results show that the system is effective and feasible, and provides a way for the vibration analysis and diagnosis of large mechanical shafting.
【学位授予单位】：内蒙古大学
【学位级别】：硕士
【学位授予年份】：2012
【分类号】：TH45;TB533;TP274.2

【相似文献】