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中文摘要：
针对聚合经验模式分解（Ensemble empirical model decomposition,EEMD）的等效滤波特性依然存在模式分量间频带重叠较大的根本缺陷,提出一种高速列车万向轴动不平衡动态检测的新方法。该方法的核心是对万向节安装机座的振动信号进行EEMD分解得到基本模式分量,应用基本模式分量信号来构造Hankel矩阵,对该矩阵进行正交化奇异值（Singular value decomposition,SVD）分解,以奇异值关键叠层作为奇异值的选择准则对信号进行重构,应用重构信号的傅里叶谱来检测高速列车万向轴的动不平衡,消除EEMD分解模式频带重叠对故障特征的淹没和混淆效应,提高了谱的清晰度,凸显了故障特征。应用万向轴动不平衡试验数据对该方法进行试验验证,结果表明,该方法能够有效检测万向轴动不平衡引起的故障特征和万向轴的固有振动特征,与纯EEMD方法相比,该方法在谱的清晰度和故障表征力上得到了显著提高。
英文摘要：
A new method of detecting dynamic imbalance with cardan shaft in the high-speed train is proposed applying the combination between ensemble empirical model decomposition（EEMD）, Hankel matrix and singular value decomposition（SVD） contrary to the aliasing defect between the adjacent intrinsic model functions existing in the EEMD. The vibration acceleration signals of gimbal are decomposed through EEMD to get the different intrinsic model components. The Hankel matrix, which is constructed throng the single decomposition model component, is orthogonally executed through SVD. The key singular values are selected to reconstruct vibration signs on the base of the key stack of singular values. Fourier spectrum of the reconstructed signal is applied to detect dynamic imbalance with shaft and eliminates clutter spectrum caused by the aliasing defect between the adjacent intrinsic model functions, highlights the failure characteristics. The method is verified by test data in the condition of dynamic imbalance, the results show this method can effectively detect the fault vibration characteristics caused by cardan shaft dynamic imbalance and extract the nature vibration features. With comparison to the simple EEMD, the clarity and failure characterization force are significantly improved.
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