间歇性工作机械上滚动轴承在线故障诊断的关键问题研究

项目名称： 间歇性工作机械上滚动轴承在线故障诊断的关键问题研究

项目编号： No.51275030

项目类型： 面上项目

立项/批准年度： 2013

项目学科： 机械、仪表工业

项目作者： 程卫东

作者单位： 北京交通大学

项目金额： 78万元

中文摘要： 间歇性工作的机械上滚动轴承的在线故障诊断存在问题。主要问题是⑴现有消噪方法难于去除间歇性噪声，⑵因为轴承振动信号具有间歇性并受转速变化影响大而使故障诊断困难。本项目围绕上述关键问题提出了利用实时转速信息的技术框架；研究消除间歇性噪声的自动阶比跟踪滤波方法；探索利用包络形状畸变特征来识别故障的理论方法。本项目拟在以下方面进行重点研究，⑴信号时频分布中特定转速变化规律成分的识别及配准；⑵自动阶比跟踪滤波的理论及算法实现；⑶转速变化对包络形状畸变影响规律及包络形状畸变估计；⑷以矿用电铲为研究对象，间歇性工作机械上滚动轴承在线故障诊断的软件实现及工程应用。新的研究方法是本项目的创新点，即利用图像处理方法实现阶比成分的识别及配准的研究思路；利用信号中其它成分的畸变特征来近似包络形状畸变特征的研究思路。预期的研究成果将有利于在更多种类设备上开展滚动轴承的在线故障诊断，促进机械故障诊断技术的发展。

中文关键词： 间歇性工作机械；滚动轴承；自动阶比跟踪滤波；包络识别；在线故障诊断

英文摘要： On-line fault diagnosing of rolling element bearings of intermittent working machine still exist many problems: intermittent noise weakening the current used denoising methodologies; intermittent working of rolling element bearing and change of speed increasing the difficulties of fault diagnosis. Focusing these problems, this project offers creative technical scheme by fully employing the speed information, studies automatic order tracking filtering algorithm to eliminate intermittent noise and utilizes distortional features of envelope to recognize fault information. This project intends to mainly study following aspects: 1) Recognition and registration of specific speed component in time-frequency distribution of signal; 2) Theory and algorithm completion of automatic order tracking filtering; 3) Corresponding relation between changing speed and distortional features of envelope and estimation of distortion feature of envelope; 4) Programming special software and realizing the on-line fault diagnosing of rolling element bearings of intermittent working machine on electric mining shovel. The innovation points of this project are new research thinking: making use of the methods of image processing to realize recognition and registration of order component and studying on how to approximate the distortional feat

英文关键词： intermittent working machine；rolling element bearing；automatic order tracking filtering；envelope recognition；on-line fault diagnosis