自主致密局部特征尺度分解方法及其应用

Method of Autonomous Compact Local Characteristic-scale Decomposition and Its Applications

针对局部特征尺度分解(LCD)的模态混叠问题,提出了自主致密局部特征尺度分解(ACLCD)方法。该方法通过确定待分解信号的最小信号极值尺度来度量其信号尺度,采用新增伪极值点均匀化信号尺度,可有效抑制模态混叠的产生;引入了最优致密系数的概念,并给出了最优致密系数评价准则。研究了ACLCD方法的原理,通过仿真信号将ACLCD与LCD和EMD进行分析对比,结果表明,ACLCD在提高分量精确性和正交性、抑制模态混叠等方面具有一定的优越性。将ACLCD方法应用于转子碰摩故障的诊断,结果表明该方法有效。

The least extrema scale was defined to measure other signal scales for restraining the mode mixing problem of LCD,and by adding pseudo-extrema autonomously to homogenize the signal scales.Based on this,a novel method of ACLCD was proposed.Compact coefficient was introduced, and the optimal evaluation criteria of compact coefficient was given.The paper firstly studied the the-ory of ACLCD,then simulation experiments were used to compare the performance of ACLCD with LCD and empirical mode decomposition（EMD）.The results indicate that ACLCD is more efficient in improving the veracity,orthogonality in components and inhibiting the mode mixing than that of LCD and EMD.Finally,the proposed method was applied to diagnose the rotor with rub-impact fault suc-cessfully which indicates the effectiveness of ACLCD.