Time frequency domain time varying structure modal parameter identification method based on time varying common demominator model

Abstract

The invention relates to a time frequency domain time varying structure modal parameter identification method based on a time varying common demominator model and belongs to the technical field of structural dynamics. Firstly, structural dynamics response signals measured and obtained by aircraft or spacecraft structures with the time varying characteristics under the work situations are analyzed in a time frequency mode to obtain a time relative power spectral function of non-parametric evaluation corresponding to time varying structures. Then, the time varying common demominator model is used as a parametric model of the time varying structural dynamics to evaluate to-be-evaluated parameters of the time varying common demominator model through a least squares methods of the time domain. Finally, the evaluated to-be-evaluated parameters of the time varying common demominator model is utilized to calculate the model frequency and the model damping ratio corresponding to the time varying common demominator model. The time frequency domain time varying structure modal parameter identification method based on the time varying common demominator model is suitable for model parameters recognition of time varying structure in the field of aircraft and spacecraft engineering application and has the advantages of being simple and convenient to use. Furthermore, users are low in participation degree.

Description

technical field [0001] The invention relates to a time-frequency domain time-varying structural modal parameter identification method based on a time-varying common denominator model, which belongs to the technical field of structural dynamics. Background technique [0002] In real production and life, many engineering structures show such time-varying characteristics, such as the axle system in the train excitation, the launch vehicle with the liquid fuel gradually reduced during the flight, the aircraft under the aerodynamic additional effect, the flexible deployable Geometrically variable spacecraft, rotating machinery, etc. [0003] In the field of domestic aerospace, new-generation spacecraft such as large-scale space stations, new-generation launch vehicles, and large flexible deployable satellites have been included in my country's latest aerospace development plan, and will become the main direction of China's spacecraft development in the next few decades. The stru...

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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 modal parameter identification methods of time-frequency domain and time-varying structures based on time-frequency analysis are all non-parametric. Although some methods can identify the modal frequencies of time-varying structures very well, the non-parametric methods are different. The degree depends on the user's subjective consciousness and experience, and there is no good way to identify the modal damping ratio of time-varying structures under random excitation

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