ANALYSIS HOW STOCHASTIC SUBSPACE IDENTIFICATION BRINGS FALSE MODES AND MODE ABSENCE

Parameter identification is currently one of the main research topics in the area of structural health monitoring. Stochastic subspace identification is a novel approach developed recent years. It can identify modal parameters of linear structure from ambient vibration of structure. Stochastic subspace identification does not involve any iteration and the only one parameter to be decided is the rank of system. So the method receives more and more attention. But stochastic subspace identification is not perfect, it has some disadvantage such as false modes and mode absence. False modes are the primary ones. These disadvantages distort the identification results. So distinguishing false modes is the key to develop stochastic subspace identification in theory and application. To do this, the first step is to analyze how false modes come into being in stochastic subspace identification. The paper presents the analysis. Research indicates that there are two sources that bring about false modes. One is the algorithm of stochastic subspace identification, the other is that input does not satisfy the assumption of stochastic subspace identification, which assume that input is zero mean white noise and/or output is contaminated by non-white noise. During analyzing, a numerical simulation is adopted.