【摘要】 应用关联维数、李亚谱诺夫指数等非线性多参数对旋转机械故障诊断进行研究。对所采集的模拟旋转机械振动故障信号,运用相空间重构理论对其时间序列重构。为使重构相空间能充分地反映系统运动特征,对不同故障信号的时间延迟与嵌入维数确定问题进行研究,计算出不同故障信号的关联维数、李亚谱诺夫指数、复杂度和近似熵四个非线性特征量。在此基础上对四个非线性参数进行融合,并定义为非线性度,用这一特征量对故障信号特征进行提取与识别。由于非线性度是关联维数、李亚谱诺夫指数、复杂度和近似熵多参数的综合,更有利于分析识别故障信号,增强可靠性。研究表明:故障类型不同,非线性度指数不同,验证了这一非线性特征量是表征不同故障信息的有效参数。此研究为复杂旋转机械故障诊断提供一种识别方法。更多还原

【Abstract】 The nonlinear multi-parameters of the correlation dimension and Lyapunov exponent and so on are applied in the research of the fault diagnosis of rotating machines.By using theory of phase space reconstruction,simulating fault signal of rotating machine is reconstructed.In order to reconstruct the phase space which can be adequately reflect the movement characteristics of the system,the time delay and embedding dimension are discussed emphatically,the four nonlinear values of correlation dimension,Lyapunov exponent,complexity and approximate entropy are calculated.On this basis,four nonlinear parameters are fused,and the characteristic quantities of non-linearity is introduced for extraction and recognition of the fault signal characteristic.Because the nonlinearity is the syntheses of multi parameters of correlation dimension,Lyapunov exponent,complexity and the approximate entropy,it will be more conducive to recognize and analyze fault signal,to enhance the reliability.Studies shows that,the fault type is different,nonlinearity is significantly different,which verifies that the nonlinear feature quantities are effective parameters for fault information,and thus a more effective way is provided for studying the fault diagnosis of complexity rotating machinery.更多还原