EWT-SVD在高速列车万向轴动不平衡检测中的应用

摘要

万向轴是高速列车传动系统的核心部件,其动不平衡检测对保障列车运行安全具有重要意义.万向轴动不平衡特征主要体现在特征频率中,针对该信号的故障特征频率提取,引入经验小波变换(empirical wavelet transform, EWT)与奇异值分解(singular value decomposition,SVD)算法.该算法利用EWT构造一组小波滤波器组提取信号的固有模态分量,并通过Hilbert变换得到每个单分量信号的瞬时频率与瞬时幅值,使用SVD结合奇异熵增量谱确定重构阶数并对每个单信号进行重构消噪.通过构造一仿真信号对算法的有效性与可行性进行验证,并将该方法运用于万向轴动不平衡检测中,结果表明:该方法能准确地提取信号的特征频率,使得谱线分辨力得到提高,可有效地应用于万向轴动不平衡检测中.%Cardan shaft is a central part of high-speed train transmission system and the detection of its dynamic imbalance is of great significance to the operation security of the train. The dynamic imbalance features of the cardan shift are mainly reflected in characteristic frequency. The empirical wavelet transform (EWT) and the singular value decomposition (SVD) algorithm are introduced for the fault characteristic frequency extraction of signal. The algorithm utilizes EWT to construct a set of wavelet filter group to extract the intrinsic modal components of signal, obtains the instantaneous frequency and instantaneous amplitude of each single-component signal through Hilbert transform and uses SVD combined with the incremental spectrum of singularity entropy to determine reconstruction order and denoise and reconstruct each signal component. The effectiveness and feasibility of the algorithm are verified by constructing a simulation signal and then the algorithm is applied to the detection of dynamic imbalance of cardan shaft. The results show that the proposed algorithm can accurately extract the characteristic frequency of the signal and improve the resolution of spectral line, thus it can be effectively applied to the detection of dynamic imbalance of cardan shaft.