Frequency response functions are often utilized to characterize a system's dynamic response. For a wide range of engineering applications, it is desirable to determine frequency response functions for a system under stochastic excitation. In practice, the measurement data is contaminated by noise and some form of averaging is needed in order to obtain a consistent estimator. With Welch's method, the discrete Fourier transform is used and the data is segmented into smaller blocks so that averaging can be performed when estimating the spectrum. However, this segmentation introduces leakage effects. As a result, the estimated frequency response function suffers from both systematic (bias) and random errors due to leakage. In this paper the bias error in the H_1 and H_2-estimate is studied and a new method is proposed to derive an approximate expression for the relative bias error at the resonance frequency with different window functions. The method is based on using a sum of real exponentials to describe the window's deterministic autocorrelation function. Simple expressions are derived for a rectangular window and a Hanning window. The theoretical expressions are verified with numerical simulations and a very good agreement is found between the results from the proposed bias expressions and the empirical results.
With growing demands on product performance and growing complexity of engineering structures, efficient tools for analyzing their dynamic behavior are essential. Linear techniques are well developed and often utilized. However, sometimes the errors due to linearization are too large to be acceptable, making it necessary to take nonlinear effects into account. In many practical applications it is common and reasonable to assume that the nonlinearities are highly local and thus only affect a limited set of spatial coordinates. The purpose of this paper is to present an approach to finding the spatial location of nonlinearities from measurement data, as this may not always be known beforehand. This information can be used to separate the underlying linear system from the nonlinear parts and create mathematical models for efficient parameter estimation and simulation. The presented approach builds on the reverse-path methodology and utilizes the coherence functions to determine the location of nonlinear elements. A systematic search with Multiple Input/Single Output models is conducted in order to find the nonlinear functions that best describe the nonlinear restoring forces. The obtained results indicate that the presented approach works well for identifying the location of local nonlinearities in structures. It is verified by simulation data from a cantilever beam model with two local nonlinearities and experimental data from a T-beam experimental set-up with a single local nonlinearity. A possible drawback is that a relatively large amount of data is needed. Advantages of the approach are that it only needs a single excitation point that response data at varying force amplitudes is not needed and that no prior information about the underlying linear system is needed.
Parametric Resonance Vibration in cables of cable-stayed bridges is mainly studied when the excitation frequency is close to or twice the cable natural frequency. It is, however, important to consider other cases for this frequency relationship, since among other factors, cable-parametric resonance vibrations are strongly depending on the displacement amplitude at the cable supports. Consequently, the present research work is focused on determining, by experimental and numerical analysis, the instability conditions for stay cables subjected to parametric resonance within a wide range of frequency ratios. This is accomplished, by finding the minimum displacement required at the cable supports in order to induce non-linear vibration of considerable amplitude at the cable. Once the cable characteristics (geometry, material properties, inherent damping and initial tensile preload) are known, the instability conditions are identified and expressed in a simplified and practical way in a diagram. Numerical results are compared to those obtained by experimental analysis carried out on a simplified scaled model (1:200) of the Öresund Bridge. A good agreement between numerical and experimental results is found.
When dealing with nonlinear dynamical systems, it is important to have efficient, accurate and reliable tools for estimating both the linear and nonlinear system parameters from measured data. An approach for nonlinear system identification widely studied in recent years is "Reverse Path". This method is based on broad-band excitation and treats the nonlinear terms as feedback forces acting on an underlying linear system. Parameter estimation is performed in the frequency domain using conventional multiple-input-multiple- output or multiple-input-single-output techniques. This paper presents a generalized approach to apply the method of "Reverse Path" on continuous mechanical systems with multiple nonlinearities. The method requires few spectral calculations and is therefore suitable for use in iterative processes to locate and estimate structural nonlinearities. The proposed method is demonstrated in both simulations and experiments on continuous nonlinear mechanical structures. The results show that the method is effective on both simulated as well as experimental data.
Accurate estimates of cutting forces are important in the evaluation of different cutting tool geometries and concepts. However, dynamic influences from the measurement system affect the result, which can make the obtained cutting force data erroneous and misleading. This article presents a method to obtain an inverse filter which compensates for the dynamic influences of the measurement system. Using this approach, unwanted dynamic effects of the measurement system can be counteracted, making it possible to retain information related to the cutting forces contained in the high frequency region. The advantage of the proposed method is illustrated by comparing simulated, inverse- and low-pass filtered forces to unfiltered forces under different cutting conditions. The results show that inverse filtering increases the usable frequency range of the force dynamometer and thereby provide more reliable results compared to both low-pass and unfiltered forces.
The response spectra are widely used in the damage assessment of non-Gaussian random vibration environments and the derivation of damage equivalent accelerated test spectrum. The effectiveness of the latter is strongly affected by modal parameter uncertainties, multiple field data processing, and the nonsmooth shape of the derived power spectral density (PSD). Optimization of accelerated test spectrum derivation based on dynamic parameter selection and iterative update of spectrum envelope is presented in this paper. The extreme response spectrum (ERS) envelope of the field data is firstly taken as the limiting spectrum, and the corresponding relationship between damping coefficient, fatigue exponent, and damage equivalent PSD under different test times is constructed to achieve the dynamic selection of uncertain parameters in the response spectrum model. Then, an iterative update model based on the weighted sum of fatigue damage spectrum (FDS) error is presented to reduce the error introduced by the nonsmooth shape of the derived PSD. The case study shows that undertest can be effectively avoided by the dynamic selection of model parameters. The weighted error is reduced from 80.1% to 7.5% after 7 iterations. Particularly, the error is close to 0 within the peak and valley frequency band.© 2021 Fei Xu et al.