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Schematic operating diagram of experimental modal parameters extraction, including the finite element updating procedure
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The capacity to detect changes in modal properties caused by structural response from those resulting from noises (environment, test conditions, etc.) is a major issue in vibration analysis. For timber-based structures monitoring, high uncertainty ratios in the measurements may prevent efficient modal parameter identification. Therefore, assessment...
Citations
... The PolyMax modal parameter identification method can be classified under the frequency domain method. It is a modal parameter identification method that combines the advantages of the least squares complex frequency domain method (LSCF) and the least squares complex exponential method (LSCE) [24]. The discrete time-frequency domain model can avoid the numerical ill-posed problem well, and the obtained modal frequency, modal participation factor, and damping ratio (strong and weak damping applications are ideal) have high accuracy. ...
This paper takes the frame as the research object and explores the vibration characteristics of the frame to address the vibration problem of a 1-MSD straw-crushing and residual film recycling machine in the field operation process, and an accurate identification of the modal parameters of the frame is carried out to solve the resonance problem of the machine, which can achieve cost reduction and increase income to a certain extent. The first six natural frequencies of the frame are extracted by finite element modal identification and modal tests, respectively. The rationality of the modal test results is verified using the comprehensive modal and frequency response confidences. The maximum frequency error of modal frequency results of the two methods is only 6.61%, which provides a theoretical basis for the optimal design of the frame. In order to further analyze the resonance problem of the machine, the external excitation frequency of the machine during normal operation in the field is solved and compared with the first six natural frequencies of the frame. The results show that the first natural frequency of the frame (18.89 Hz) is close to the external excitation generated by the stripping roller (16.67 Hz). The first natural frequency and the volume of the frame are set as the optimization objectives, and the optimal optimization scheme is obtained by using the Optistruct solver, sensitivity method, and grey correlation method. The results indicate the first-order natural frequency of the optimized frame is 21.89 Hz, an increase of 15.882%, which is much higher than the excitation frequency of 16.67 Hz, and resonance can be avoided. The corresponding frame volume is 9.975 × 107 mm3, and the volume reduction is 3.46%; the optimized frame has good dynamic performance, which avoids the resonance of the machine and conforms to the lightweight design criteria of agricultural machinery structures. The research results can provide some theoretical reference for this kind of machine in solving the resonance problem and carrying out related vibration characteristics research.
... It not only carries the power of engines, planting device, and other mechanisms but also vibrates due to the excitation of the ground, engine, conveying mechanism, and adding and dropping mechanism during operation. 1,2 When the excitation frequency generated by these mechanisms is close to the natural frequency of the chassis frame, the whole body will produce a resonance phenomenon. This will affect the working performance and service life of the whole machine. ...
Modal parameter analysis is a common method for structural characteristic analysis. To accurately identify the modal parameters that describe the vibration characteristics of the structure, the chassis frame of the rice transplanter is taken as the research object. The least squares complex frequency domain method, the admittance circle method, and the modal peak picking method are used to process the measured vibration signal. The modal parameters, such as the natural frequency, damping ratio, and modal shape of the chassis frame, are obtained. Except for the eight-order frequency error greater than 10%, the errors of all other orders are all less than 10%. In the modal test of the frame structure of the rice transplanter, the identification results of the modal parameters are generally reliable. The recognition accuracy of the PolyLeast-Squares Complex Frequency (PolyLSCF) algorithm is lower than the recognition accuracy of the admittance circle method and modal peak picking. The values of some off-diagonal elements in the modal mass matrix calculated by the PolyLSCF algorithm and the admittance circle method are greater than 0.2. The diagonal elements of the modal mass matrix calculated by the modal peak picking method are all 1.
... Throughout the bridge safety accidents at home and abroad, the reasons are many, mainly including: poor dynamic design of bridge structures, lack of strict control during construction, cutting corners (Hamdi et al., 2021), inadequate maintenance and management during operation, The real-time monitoring technology is immature and bears instantaneous loads. Many bridge collapse accidents have caused widespread concern in the society. ...
After the bridge is completed, the structural materials will be gradually eroded or aged under the influence of climate, temperature, and building environment. Under long-term static and dynamic loads, the structural strength and stiffness of bridge structures, including bridge deck and bridge support, will decrease with the accumulation of time. Bridge modal parameter identification is not only the premise and foundation of health monitoring, but also the main part of bridge structure dynamic identification. Therefore, this paper proposes a bridge modal parameter identification model based on Bayesian method. The model fully considers the uncertainty of parameters and the selection of modal parameters, and identifies more local information through the probability distribution of model parameters and a posteriori confidence. The reliability of the bridge is monitored in real time through the Bayesian dynamic model, and the monitoring error is only 0.01, which can realize high-precision bridge modal parameter identification.
In this work, dynamic mode decomposition (DMD) was applied as an algorithm for determining the natural frequency and damping ratio of viscoelastic lattice structures. The algorithm has been developed based on the Hankel alternative view of Koopman and DMD (HAVOK-DMD). In general, the Hankel matrix is based on time-delay embedding, which is meant for the hidden variable in a time series data. Vibration properties of a system could be then estimated from the eigenvalues of approximated Koopman operator. Results of the proposed algorithm was firstly validated with those of the traditional discrete Fourier transform (DFT) approach and half power bandwidth (HPBW) by using analytical dataset of multi-modal spring-mass-damper system. Afterwards, the algorithm was further used to analyze dynamic responses of viscoelastic lattice structures, in which data from both experimental and numerical finite element (FE) model were considered. It was found that the DMD based algorithm could accurately estimate the natural frequencies and damping ratios of the examined structures. In particular, it is beneficial to any dataset with limited amounts of data, whereby experiments or data gathering processes are expensive.
PurposeIdentifying unknown parameters has long been significant in the field of engineering, as they can be used for fault diagnosis and the development of numerical models. This study aims to determine the unknown parameters of mechanical models by applying deep neural networks (DNNs) using frequency–response functions (FRFs).Methods
The proposed network consists of several DNNs that estimate unknown parameters. The inputs of the DNNs requires the initial values of the unknowns. The estimated parameters by the DNNs were used to calculate the outputs. The magnitude and phase of the FRF obtained from an experiment were used as target data. To optimize the performance of the DNNs, Bayesian optimization is employed to search for appropriate weight factors in the loss function and hyperparameters in the DNNs. Additionally, the estimated parameters were adopted as the optimal parameters after the difference between the output and target data satisfied the stopping criterion.ResultsThe performance of the DNN-based method was confirmed for lumped and distributed parameter models with noise, which could significantly challenge the parameter identification. The proposed method showed high accuracy within 5% for both models and was validated in terms of convergence by simulations and experiments. Furthermore, we compared the proposed method with the conventional optimization methods across various noise levels and initial values.Conclusion
The proposed method successfully estimated unknown parameters under severe noise and initial conditions compared with the other methods.
