NExT/ARMA-based structural response analysis method

Abstract

The invention relates to an NExT / ARMA based structural response analysis method and a technology for modal parameter identification through structural vibration response, in particular to the technology for modal parameter identification of the structure by directly utilizing structural response under ambient excitation. The method is characterized by first detecting the structural response under ambient excitation; then utilizing NExT to convert the response signal to the damped free cross correlation function; and subsequently using the cross correlation function as the input data and identifying the modal parameters (frequency, mode vectors and damping ratio) of the structure by applying the ARMA time series model. The method can better handle the non-linear and astable response signals with multiple degrees of freedom under non-free vibration, and modal parameter identification, can be applied to signal processing and modal parameter identification in such fields as civil engineering, aerospace, autocontrol, mechanical engineering, bridge engineering, hydraulic engineering and the like, has the capabilities of improving the signal to noise ratio of the data and resisting interference and has the characteristic of more accurate modal parameter identification.

Description

technical field [0001] The present invention relates to a method for signal analysis and modal parameter identification of structural vibration response, in particular to a structural response analysis based on direct use of environmental excitation (cross-correlation function theory) / autoregressive moving average model (NExT / ARMA) method. Background technique [0002] With the frequent occurrence of natural disasters such as earthquakes and hurricanes and the decline in the performance of buildings (structures), various structural damages are likely to occur. Various engineering accidents such as house collapses, sudden cracks and even breaks in bridges are not uncommon. The economy and people's life and property have caused great losses. Therefore, it is particularly important to detect and diagnose these buildings (structures) in advance; to conduct overall real-time monitoring of large-scale engineering structures, establish a structural health monitoring system, and re...

AI Technical Summary

Problems solved by technology

The disadvantage is that for the time series to establish the ARMA model, a random process with a steady state and 0 mean is required

But in fact, many time series do not satisfy the zero mean value and stationarity. Although the zero mean value and nearly stable random process can be obtained through methods such as zeroization and elimination of trend items, the processed time series are used for modeling and Identification parameters will cause a large deviation between the identification result and the actual value

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