Method for identifying time-varying structure modal frequency based on time frequency distribution map

Abstract

The invention relates to a method for identifying a time-varying structure modal frequency based on a time frequency distribution map. The method comprises the following steps of: 1, acquiring structural dynamic response signals of an identified structure and setting sampling time and sampling frequency; 2, performing time frequency transformation on each response signal to obtain a time frequency distribution coefficient and drawing the time frequency distribution map; 3, writing the time frequency distribution coefficient into a corresponding energy distribution form and rearranging the coefficient as a column vector; 4, determining a time frequency distribution region corresponding to the response containing each-order time-varying modal frequency for identification according to the time frequency distribution map of each response; 5, extracting parts with the highest energy time frequency distribution corresponding to the each-order time-varying modal frequency from the time frequency distribution map by using proper time frequency window functions respectively; 6, estimating the each-order time-varying modal frequency by using a weighting nonlinear least square method; and 7, performing error analysis on the identification result. The method has the advantages of clear physical significance, simple and convenient use, high applicability and high anti-interference capability.

Description

technical field [0001] The invention relates to a time-varying structural mode frequency identification method based on a time-frequency distribution diagram, belonging to the field of structural dynamic mode parameter identification. Background technique [0002] Strictly speaking, all structures (systems) in the real physical environment are time-varying, but are divided into different levels on the time scale. The time-varying structure currently mainly studied refers to the structure that changes its shape rapidly or some important parameters change rapidly during the working process. The fast expression here cannot ignore the effect of inertial force. Many engineering structures exhibit such time-varying characteristics, such as train-axle systems in train excitation, launch vehicles with liquid fuel gradually reduced during flight, aircraft under aerodynamic additional effects, flexible and deployable geometrically variable spacecraft, rotating machinery etc. [0003...

AI Technical Summary

Problems solved by technology

There are two defects in this kind of method: first, there is the problem of selection of observation data and forgetting factor (algorithm), which requires a compromise between recognition accuracy and tracking ability, and the adaptation to the correlation selection of different structures Second, this type of method comes from the traditional modal parameter identification method, which requires the response information of both the input and output of the structure, so it is difficult to apply it to a vehicle that can only get the output response signal. Structural modal parameter identification of

However, some inherent problems limit its further development and application: first, the assumption of short-term invariance limits the application of such methods to the identification of rapidly changing and abrupt changes; second, such methods require fixed and explicit mathematical Models, such as state-space models, autoregressive moving average models of time series, etc. Therefore, the problem of model order determination is very prominent in identification, and the uncertainty of model order will introduce false modes without physical meaning, resulting in inaccurate identification results. Therefore, issues such as reasonable selection of model order and judgment of false modes need further in-depth research; third, as two mainstream methods of modal parameter identification based on short-term invariant assumptions—recursive random subspace identification The method and the time-dependent autoregressive moving average model have some other problems: the stacked subspace method based on the state-space model inevitably uses QR decomposition, eigenvalue decomposition (EVD) or singular value decomposition (SVD) techniques, which are It will inevitably bring about the complexity of the numerical implementation of the method. For large-scale engineering structures, especially for problems with online and fast identification requirements, further research is needed; the research on identification methods based on time series models cannot avoid the parameter tracking algorithm. Design, although various improved least squares methods and various filtering methods are continuously proposed, but when the same model uses different tracking algorithms, and different models apply the same algorithm, the results are very different

[0015] The existing structural modal frequency identification methods based on time-frequency analysis are not universal for some structures that can write analytical expressions; on the other hand, the process of modal frequency identification is relatively complicated and has no obvious physical meaning

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