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针对电梯导靴振动信号采用经验模态分解(Empirical Mode Decomposition,EMD)难以直接提取早期微弱故障特征的问题,提出基于奇异值分解(Singular Value Decomposition,SVD)优化经验模态分解的电梯导靴振动信号故障特征提取方法。该方法首先对原始信号进行SVD分解,通过奇异值贡献率原则来确定相空间重组的最佳Hankel矩阵结构,利用曲率谱原则与奇异值贡献率原则相结合来确定有效奇异值的阶次;筛选出包含主要故障信息的奇异值进行信号重构,得到剔除噪声信号与光滑信号的突变信号;然后对突变信号进行EMD分解,得到信号的本征模态函数(Intrinsic Mode Function,IMF)分量。最后,对IMF分量作Hilbert变换,求得其Hilbert边际谱,从而获得电梯导靴故障特征频率信息。仿真结果表明该方法有效改善了EMD难以直接提取早期微弱故障特征的问题,更准确地提取了振动信号的故障特征频率,验证了所述方法的有效性。
In order to solve the problem that it is difficult to directly extract the characteristics of early weak faults by the empirical mode decomposition (Empirical Mode Decomposition) for the vibration signals of the guide shoe of the elevator, the vibration of the guide shoes based on Singular Value Decomposition (SVD) optimization empirical mode decomposition Signal fault feature extraction method. In this method, the original signal is decomposed by SVD. The best Hankel matrix structure of phase space reorganization is determined by the principle of singular value contribution rate. The order of effective singular value is determined by the combination of curvature spectrum principle and singular value contribution rate. A singular value containing the main fault information is used to reconstruct the signal to obtain the abrupt signal that rejects the noise signal and the smooth signal. Then, the abrupt signal is decomposed by EMD to obtain the Intrinsic Mode Function (IMF) component of the signal. Finally, the Hilbert transform of the IMF components is used to obtain the Hilbert marginal spectrum, so as to obtain the fault frequency information of the elevator guide shoe. The simulation results show that the proposed method can effectively improve the problem that EMD can not directly extract the early weak fault features, and more accurately extract the fault feature frequency of the vibration signal, which verifies the effectiveness of the proposed method.