Empirical modeless decomposition and modal identification method for nonstationary vibration processes

An empirical data decomposition method is developed in this study to accurately identify modeless component and intrinsic vibration modal terms separately from vibration processes, which are characterized by nonlinearity and nonstationarity. Two key innovations related to this method are proposed. One of them is associated with the definition of root mean square instantaneous amplitude, which is adopted as a meaningful supplement for vibration mode identification. It also acts as a critical criterion to visualize the nonstationarity of some vibration processes. The other innovation is the use of a predetermined maximum intrinsic time window and cubic spline interpolation technique for the empirical data decomposition method with the aim to decompose a modeless component from nonstationary processes. Unlike in other decomposition methods, a modeless component should be decomposed prior to modal analysis. The requirement of using spurious harmonics to represent nonlinear and nonstationary signals is fully eliminated via these two key innovations, thereby increasing the accuracy of the subsequent modal identification. Given the well-designed decomposition method, energy aliasing in data processing can also be effectively avoided. Significant conclusions about the complicated vibration processes composed of nonstationary and intrinsic modal terms are drawn by using the proposed empirical modeless decomposition and modal identification method.