用奇异谱和奇异熵研究数控工作台动态特征  被引量:7

Dynamic Characteristics of Numerical Control Table with Singular Spectrum and Singular Entropy

在线阅读下载全文

作  者:王林鸿[1] 吴波[2] 杜润生[2] 杨叔子[2] 

机构地区:[1]南阳理工学院机电工程系,南阳473004 [2]华中科技大学机械科学与工程学院,武汉430074

出  处:《振动.测试与诊断》2012年第1期116-119,166,共4页Journal of Vibration,Measurement & Diagnosis

基  金:国家基础研究发展计划("九七三"计划)资助项目(编号:2005CB724101)

摘  要:通过采集数控工作台振动加速度信号,对其进行奇异值分解,得到奇异谱和奇异熵。通过主分量、奇异谱和奇异熵等特征量揭示数控工作台动态特征和规律性。奇异值分解结果表明,可以用前一、两个主分量的特征来研究整个时间序列的信号特征以简化特征提取。用奇异谱陡峭程度辨别信号的复杂程度,发现驱动轴方向信号最简单,铅垂方向信号最复杂。用奇异熵值研究信号的不确定性发现:数控工作台信号既不是简单信号也不是白噪声;驱动轴方向熵值低;熵值随移动速度增加有所增加;共振或爬行等典型状态熵值明显减少。NC(numerical control) table is a complex dynamic system.By collecting vibration acceleration signals from NC table,the signals with SVD(singular value decomposition) method is analyzed,the singular spectrum is acquired and the singular entropy of the signals is calculated.The dynamic characteristics and their regulations of NC table are revealed via the characteristic quantities such as principal component,singular spectrum,singular entropy,etc.The results of SVD show that the first one or two principal components can be used to analyze the signal characteristics of the total time series in order to simplify characteristic extraction.The steep degrees of singular spectrums can be used to discriminate complex degrees of signals.The results show that the signals in direction of driving axes are the simplest and the signals in perpendicular direction are the most complex.The singular entropy values can be used to study the indetermination of signals.The results show that the signals of NC table are not simple signal nor white noise,the entropy values in direction of driving axes are lower,the entropy values increase a little bit along with the increment of driving speed and the entropy values at the classic conditions,such as resonance or creep,decrease obviously.

关 键 词:数控工作台 动态特征 奇异谱 奇异熵 

分 类 号:TH113.1[机械工程—机械设计及理论]

 

参考文献:

正在载入数据...

 

二级参考文献:

正在载入数据...

 

耦合文献:

正在载入数据...

 

引证文献:

正在载入数据...

 

二级引证文献:

正在载入数据...

 

同被引文献:

正在载入数据...

 

相关期刊文献:

正在载入数据...

相关的主题
相关的作者对象
相关的机构对象