自适应掩膜信号集成局部特征尺度分解及其应用

局部特征尺度分解（LCD）是为克服经验模态分解（EMD）中均值曲线构造的不足而提出的一种自适应信号分解方法，已被应用于机械故障诊断领域.但LCD存在与EMD类似的模态混叠问题，为此，基于均匀相位差掩膜信号构造，提出了自适应掩膜信号集成局部特征尺度分解（AMSELCD），该方法不仅能够将一个复杂信号自适应地分解为若干个本征模态函数和一个剩余项之和，而且能够有效地解决LCD的模态混叠现象.通过仿真信号分析，将AMSELCD与现有多种抑制模态分解方法进行了对比，结果表明了所提方法的有效性和优越性.最后，针对滚动轴承和转子碰摩故障振动信号的调制特征，将所提AMSELCD方法应用于转子碰摩和滚动轴承的故障诊断，对比和实验分析结果进一步验证了所提方法的有效性和优越性.

Adaptive Mask Signal-Based Local Characteristic-Scale Decomposition and Its Application

Local characteristic-scale decomposition (LCD) is an adaptive signal decomposition method proposed to overcome the shortcomings of mean curve construction in empirical mode decomposition (EMD) and has been applied to mechanical fault diagnosis.However,LCD also has the mode mixing problem that exists in EMD.Based on the construction of masking signal with uniform phase difference,the adaptive mask signal ensemble local characteristic-scale decomposition (AMSELCD) is proposed in this paper to adaptively decompose a complex signal into several intrinsic mode functions and a residue,which can effectively alleviate the mode mixing phenomenon of LCD.AMSELCD is compared with various existing methods for restraining mode mixing through simulation signal analysis and the results show the effectiveness and superiority of the proposed method.Finally,aiming at the modulation characteristics of fault signals of rolling bearing and rotor rubbing,the proposed AMSELCD method is applied to the fault diagnosis of rotor rubbing and rolling bearing,and the experimental comparison analysis results further verify the effectiveness and superiority of AMSELCD.