基于微分算子的局部特征尺度分解方法及其应用

摘要

针对局部特征尺度分解(LCD)方法在目标信号所含频率分量较接近时分解能力降低,易出现模态混淆现象,从而导致内禀尺度分量失去原有物理意义的问题,提出了基于微分算子的局部特征尺度分解(DOLCD).DOLCD 在对目标信号进行分解前,先将目标信号进行一阶微分,则在频率比一定的情况下,可提升分解能力及抑制模态混淆能力.研究了 DOLCD 方法的原理,通过仿真信号模型将DOLCD 与 LCD 的分解能力进行对比分析,结果表明,DOLCD 方法在提高分解能力,抑制模态混淆等方面具有一定的优越性,并将 DOLCD 方法应用于转子不对中故障的诊断,结果表明该方法有效.%A novel non-stationary signal method differential operator based LCD(DOLCD)was proposed for improving the LCD method,of which,when the target signal contained components with frequencies close to each other,the decomposition ability was decreased,mode mixing phenomenon would easily occur and the physical meaning of intrinsic scale components decomposed were lost.A differential operator was used to differentiate the target signal in time domain,then the differentiated target signal was decomposed with DOLCD,then in the certain frequency ratio,the decomposition ability was enhanced,and the mode mixing was inhibited.The paper firstly studied the theory of DOLCD,then simulation experiments were used to compare the decomposition ability of DOLCD with LCD.The results indicate that DOLCD is more efficient in improving the decomposition ability,and inhibiting the mode mixing than LCD.Finally,the proposed method was applied to diagnose the rotor with misalignment faults successfully which indicated the effectiveness of DOLCD.