针对轧机故障信号的非线性、非平稳特征,研究了一种基于经验模态分解和AR模型功率谱相结合的分析方法。该方法首先对轧机主传动系统的故障信号进行EMD分解,然后通过选取含有故障信息的IMF分量进行AR模型的功率谱分析,从而提取出故障频率,判断引起故障的原因。通过仿真信号和实例验证了该方法的有效性。
Abstract
According to the non-stationary and non-linear characteristic of rolling mill fault signal, an analysis method based on EMD and AR spectrum is studied. First, the original vibration signals are decomposed by EMD. Subsequently, the IMFs relating to fault information are applied to AR spectrum analysis. The result of this method is the AR spectrum of relating IMFs, from which the fault frequencies can be extracted, and the locations of the faults can be identified. The method is applied to the rolling mill fault signals, then the state of the vibration can be acquired. The validity of the method is demonstrated by simulation and case analysis.
关键词
计量学 /
故障诊断 /
经验模态分解 /
AR谱 /
轧机
Key words
Metrology /
Fault diagnosis /
Empirical mode decomposition /
AR spectrum /
Rolling mill
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
［1］王志刚,李友荣.小波分析—AR谱及其工程应用［J］.振动与冲击,2001,20(1):85-88.
［2］Huang N E, Shen Z, Long S R, et al. The empirical mo-de decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis［J］.Proceeding of Royal Society London,1998,454(1971):903-995.
［3］Huang N E, Shen Z, Long S R.A new view of nonlinear water waves: the Hilbert spectrum［J］. Annual Review of Fluid Mechanics,1999,31(1):417-457.
［4］Huang W, Shen Z, Norden E, et al. Nonlinear Indicial Response of Complex Nonstationary Oscillations as Pul-monary Hypertension Responding to Step Hypoxia［J］.Pro-ceedings of the National Academy of Sciences,USA,1999,96(5):1834-1839.
［5］谢平, 王欢, 杜义浩.基于EMD和Wigner-Ville分布的机械故障诊断方法研究［J］.计量学报, 2010,31(5):390-394.
［6］周鹏, 秦树人.多分辨Hilbert边际谱在旋转机械故障诊断中的应用［J］.计量技术,2007,(2):72-74.
［7］李辉, 郑海起, 唐力伟. 基于EMD和功率谱的齿轮故障诊断研究［J］.振动与冲击, 2006,25(1):133-136.
［8］杨叔子, 吴雅, 轩建平, 等.时间序列分析的工程应用(上册)［M］.武汉:华中科技大学出版社, 2007,175-222.
［9］李国华, 张永忠. 机械故障诊断［M］.北京:化学工业出版社, 1991,120-133.
基金
河北省自然科学基金-钢铁联合研究基金（F2009000500)
PDF(1473 KB)
