scholarly journals Modal Parameter Identification from Output Data Only: Equivalent Approaches

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Joseph Lardies
Keyword(s):  
Parameter Identification ◽  
Hankel Matrix ◽  
Noisy Data ◽  
Experimental Results ◽  
Modal Parameters ◽  
Modal Parameter ◽  
Output Data ◽  
Modal Parameter Identification

The problem of modal parameter identification from output data only is presented. To identify the modal parameters different algorithms are presented: the block Hankel matrix and its shifted version and the block observability and block controllability matrices and their shifted version. These algorithms are derived from properties of the subspace approach. It is shown in the paper that these algorithms give the same results even in the noisy data case. Numerical and experimental results are presented showing the effectiveness of the procedure. In particular a microsystem constituted of a perforated microplate is analysed.

scholarly journals Noise Reduction for Modal Parameter Identification of the Measured FRFs Using the Modal Peak-Based Hankel-SVD Method

2020 ◽  
Vol 2020 ◽  
pp. 1-21
Author(s):  
Tianxu Zhu ◽  
Chaoping Zang ◽  
Gengbei Zhang
Keyword(s):  
Parameter Identification ◽  
Noise Reduction ◽  
Flat Plate ◽  
Modal Identification ◽  
Modal Parameters ◽  
Modal Parameter ◽  
Noise Elimination ◽  
Modal Parameter Identification ◽  
Modal Test ◽  
Svd Method

The measured frequency response functions (FRFs) in the modal test are usually contaminated with noise that significantly affects the modal parameter identification. In this paper, a modal peak-based Hankel-SVD (MPHSVD) method is proposed to eliminate the noise contaminated in the measured FRFs in order to improve the accuracy of the identification of modal parameters. This method is divided into four steps. Firstly, the measured FRF signal is transferred to the impulse response function (IRF), and the Hankel-SVD method that works better in the time domain rather than in the frequency domain is further applied for the decomposition of component signals. Secondly, the iteration of the component signal accumulation is conducted to select the component signals that cover the concerned modal features, but some component signals of the residue noise may also be selected. Thirdly, another iteration considering the narrow frequency bands near the modal peak frequencies is conducted to further eliminate the residue noise and get the noise-reduced FRF signal. Finally, the modal identification method is conducted on the noise-reduced FRF to extract the modal parameters. A simulation of the FRF of a flat plate artificially contaminated with the random Gaussian noise and the random harmonic noise is implemented to verify the proposed method. Afterwards, a modal test of a flat plate under the high-temperature condition was undertaken using scanning laser Doppler vibrometry (SLDV). The noise reduction and modal parameter identification were exploited to the measured FRFs. Results show that the reconstructed FRFs retained all of the modal features we concerned about after the noise elimination, and the modal parameters are precisely identified. It demonstrates the superiority and effectiveness of the approach.

Methods of Gear Damage Assessment Based on Modal Parameter Identification

2013 ◽  
Vol 819 ◽  
pp. 38-42
Author(s):  
Jin Bao Ma ◽  
Jian Yu Zhang ◽  
Xin Bo Liu
Keyword(s):  
Parameter Identification ◽  
Damage Assessment ◽  
Spur Gear ◽  
Modal Parameters ◽  
Vibration Characteristic ◽  
Modal Parameter ◽  
Damage State ◽  
Element Analysis ◽  
Modal Parameter Identification ◽  
Vibration Mechanism

With the evolution and degradation of mechanical fault, changes of the structural inherent characteristics will directly affect the overall response of system. Spur gear, which worked as the research object, is to be explored on the changes of modal parameters under different damage state. Optimum driving-point mobility and modal parameter identification is achieved by comprehensive utilization of experimental modal analysis and finite element analysis. is used to determine the experiment results is whether accurate or not. Then comparing with the differences of modal parameters, the preliminary judgment of gear damage can be made. According to the experimental data of different gears, theis taken to complete the correlation analysis and to judge the degree of the damage. The results shows that provide an effective basis for the identification of vibration mechanism and vibration characteristic of fault gear.

scholarly journals Time-Varying Modal Parameters Identification by Subspace Tracking Algorithm and Its Validation Method

2018 ◽  
Vol 2018 ◽  
pp. 1-12
Author(s):  
Haotian Zhou ◽  
Kaiping Yu ◽  
Yushu Chen ◽  
Rui Zhao ◽  
Yunhe Bai
Keyword(s):  
Parameter Identification ◽  
Spatial Clustering ◽  
Signal To Noise Ratio ◽  
Information Criterion ◽  
Modal Parameters ◽  
Identification Algorithm ◽  
Time Varying ◽  
Modal Parameter ◽  
Modal Parameter Identification ◽  
Post Process

This article presents a time-varying modal parameter identification method based on the novel information criterion (NIC) algorithm and a post-process method for time-varying modal parameter estimation. In the practical application of the time-varying modal parameter identification algorithm, the identified results contain both real modal parameters and aberrant ones caused by the measurement noise. In order to improve the quality of the identified results as well as sifting and validating the real modal parameters, a post-process procedure based on density-based spatial clustering of applications with noise (DBSCAN) algorithm is introduced. The efficiency of the proposed approach is first verified through a numerical simulation of a cantilever Euler-Bernoulli beam with a time-varying mass. Then the proposed approach is experimentally demonstrated by composite sandwich structure in a time-varying high temperature environment. The identified results illustrate that the proposed approach can obtain real modal frequencies in low signal-to-noise ratio (SNR) scenarios.

Identify Modal Parameters of a Real Offshore Platform From the Response Excited by Natural Ice Loading

2016 ◽  
Author(s):  
Wenlong Yang ◽  
Lei Li ◽  
Qiang Fu ◽  
Yao Teng ◽  
Shuqing Wang ◽  
...  
Keyword(s):  
Parameter Identification ◽  
Modal Analysis ◽  
Offshore Platform ◽  
Operational Modal Analysis ◽  
Modal Parameters ◽  
Offshore Platforms ◽  
Modal Parameter ◽  
Modal Parameter Identification ◽  
Stability Diagrams ◽  
The Stability

Experimental modal analysis (EMA) is widely implemented to obtain the modal parameters of an offshore platform, which is crucial to many practical engineering issues, such as vibration control, finite element model updating and structural health monitoring. Traditionally, modal parameters are identified from the information of both the input excitation and output response. However, as the size of offshore platforms becomes huger, imposing artificial excitation is usually time-consuming, expensive, sophisticated and even impossible. To address this problem, a preferred solution is operational modal analysis (OMA), which means the modal testing and analysis for a structure is in its operational condition subjected to natural excitation with output-only measurements. This paper investigate the applicability of utilizing response from natural ice loading for operational modal analysis of real offshore platforms. The test platform is the JZ20-2MUQ Jacket platform located in the Bohai Bay, China. A field experiment is carried out in winter season, when the platform is excited by floating ices. An accelerometer is installed on a leg and two segments of acceleration response are employed for identifying the modal parameters. In the modal parameter identification, specifically applied is the data-driven stochastic sub-space identification (SSI-data) method. It is one of the most advanced methods based on the first-order stochastic model and the QR algorithm for computing the structural eigenvalues. To distinguish the structural modal information, stability diagrams are constructed by identifying parametric models of increasing order. Observing the stability diagrams, the modal frequencies and damping ratios of four structural modes can be successfully identified from both segments. The estimated information from both segments are almost identical, which demonstrates the identification is trustworthy. Besides, the stability diagrams from SSI-data method are very clean, and the poles associated with structural modes can become stabilized at very low model order. The research in this paper is meaningful for the platforms serving in cold regions, where the ices could be widespread. Utilizing the response from natural ice loading for modal parameter identification would be efficient and cost-effective.

scholarly journals Modal Parameter Identification of Structures Using Reconstructed Displacements and Stochastic Subspace Identification

2021 ◽  
Vol 11 (23) ◽  
pp. 11432
Author(s):  
Xiangying Guo ◽  
Changkun Li ◽  
Zhong Luo ◽  
Dongxing Cao
Keyword(s):  
Parameter Identification ◽  
Modal Analysis ◽  
Moving Average ◽  
Random Fluctuation ◽  
High Frequency Component ◽  
Modal Parameters ◽  
Subspace Identification ◽  
Modal Parameter ◽  
Modal Parameter Identification ◽  
Stochastic Subspace Identification

A method of modal parameter identification of structures using reconstructed displacements was proposed in the present research. The proposed method was developed based on the stochastic subspace identification (SSI) approach and used reconstructed displacements of measured accelerations as inputs. These reconstructed displacements suppressed the high-frequency component of measured acceleration data. Therefore, in comparison to the acceleration-based modal analysis, the operational modal analysis obtained more reliable and stable identification parameters from displacements regardless of the model order. However, due to the difficulty of displacement measurement, different types of noise interferences occurred when an acceleration sensor was used, causing a trend term drift error in the integral displacement. A moving average low-frequency attenuation frequency-domain integral was used to reconstruct displacements, and the moving time window was used in combination with the SSI method to identify the structural modal parameters. First, measured accelerations were used to estimate displacements. Due to the interference of noise and the influence of initial conditions, the integral displacement inevitably had a drift term. The moving average method was then used in combination with a filter to effectively eliminate the random fluctuation interference in measurement data and reduce the influence of random errors. Real displacement results of a structure were obtained through multiple smoothing, filtering, and integration. Finally, using reconstructed displacements as inputs, the improved SSI method was employed to identify the modal parameters of the structure.

Identification Technology of Modal Parameter of a Strap-on Launch Vehicle

2016 ◽  
Vol 13 (01) ◽  
pp. 1650010
Author(s):  
Pan Liu ◽  
Yong Xie ◽  
Shao-Jing Guo ◽  
Guo-Ping Cai
Keyword(s):  
Attitude Control ◽  
Synthesis Method ◽  
Launch Vehicle ◽  
Modal Parameters ◽  
Modal Parameter ◽  
Output Data ◽  
Modal Parameter Identification ◽  
Identification Method ◽  
Modal Synthesis ◽  
Identification Techniques

It is generally needed to conduct ground modal tests over strap-on launch vehicle to provide modal parameters for the attitude control design and load calculating. These modal parameters can also provide basis of installing sense organs on the launch vehicle. This paper introduces a modal parameter identification method based on input-output data of the system, which can be used to check calculations of modal parameters after ground modal tests. In this paper, the double-compatible free-interface modal synthesis method is first used for the modeling of the system so as to get the input and output data of the system. Then the identification techniques of observer/Kalman filter identification (OKID) and eigensystem realization algorithm (ERA) are introduced. Finally, numerical simulations are carried out to demonstrate the validity of the presented identification method. Simulation results indicate that the modal parameter of the system can be effectively identified using OKID and ERA.

Modal Parameter Identification of a Rigid Frame-Continuous Girders Bridge under Ambient Excitation by Fast Bayesian FFT Method

2013 ◽  
Vol 639-640 ◽  
pp. 985-991 ◽  
Author(s):  
Jian Ping Han ◽  
Pei Juan Zheng
Keyword(s):  
Parameter Identification ◽  
Model Updating ◽  
Element Model ◽  
Modal Identification ◽  
Modal Parameters ◽  
Modal Parameter ◽  
Modal Parameter Identification ◽  
Stochastic Subspace Identification ◽  
Rigid Frame ◽  
Ambient Excitation

Bayesian theory is adopted in modal parameter identification, finite element model updating and residual capacity evaluation of the structures recently. Fast Bayesian FFT modal identification approach provides a rigorous way to obtain modal parameters and well-separated modes using the fast Fourier transform under ambient excitation. Moreover, it avoids choosing the modal order or removing false modes based on the stable diagram and has its obvious advantages. In this paper, modal parameters of a rigid frame-continuous girders bridge under ambient excitation are identified by this approach. Comparison with stochastic subspace identification (SSI) method indicates that Fast Bayesian FFT is a good approach to identify the modal parameters even for a large number of measurement channels.

A Modal Parameter Identification Method Based on Improved Covariance-Driven Stochastic Subspace Identification

2020 ◽  
Vol 142 (6) ◽  
Author(s):  
Chen Wang ◽  
Minghui Hu ◽  
Zhinong Jiang ◽  
Yanfei Zuo ◽  
Zhenqiao Zhu
Keyword(s):  
Parameter Identification ◽  
Gas Turbines ◽  
Excitation Frequency ◽  
Modal Parameters ◽  
Subspace Identification ◽  
Modal Parameter ◽  
Modal Parameter Identification ◽  
Mode Identification ◽  
Stochastic Subspace Identification ◽  
Identification Method

Abstract For the quantitative dynamic analysis of aero gas turbines, accurate modal parameters must be identified. However, the complicated structure of thin-walled casings may cause false mode identification and mode absences if conventional methods are used, which makes it more difficult to identify the modal parameters. A modal parameter identification method based on improved covariance-driven stochastic subspace identification (covariance-driven SSI) is proposed. The ability to reduce the number of mode absences and the solving stability are improved by a covariance matrix dimension control method. Meanwhile, the number of false mode identification is reduced via a false mode elimination method. In addition, the real mode complementation and the excitation frequency mode screening can be realized by a multispeed excitation method. The numerical results of a typical rotor model and measured data of an aero gas turbine validated the proposed method.

Consistent Multilevel RDT-ERA for Output-Only Ambient Modal Identification of Structures

2017 ◽  
Vol 17 (09) ◽  
pp. 1750106 ◽  
Author(s):  
Zhouquan Feng ◽  
Wenai Shen ◽  
Zhengqing Chen
Keyword(s):  
Parameter Identification ◽  
Modal Identification ◽  
Ambient Vibration ◽  
Weighted Averaging ◽  
Modal Parameters ◽  
Modal Parameter ◽  
Consistency Analysis ◽  
Modal Parameter Identification ◽  
Random Decrement Technique ◽  
Random Decrement

This paper presents an improved method called the consistent multilevel random decrement technique in conjunction with eigensystem realization algorithm (RDT-ERA) for modal parameter identification of linear dynamic systems using the ambient vibration data. The conventional RDT-ERA is briefly revisited first and the problem of triggering level selection in the RDT is thoroughly studied. Due to the use of a single triggering level by the conventional RDT-ERA, an inappropriate triggering level may produce poor random decrement (RD) functions, thereby yielding a poor estimate of modal parameters. In the proposed consistent multilevel RDT-ERA, multiple triggering levels are used and a consistency analysis is proposed to sift out the RD functions that deviate largely from the majority of the RD functions. Then the ERA is applied to the retained RD functions for modal parameter identification. Subsequently, a similar consistency analysis is conducted on the identified modal parameters to sift out the outliers. Finally, the final estimates of the modal parameters are calculated using weighted averaging with the weights set proportional to the number of RD segments extracted from the corresponding triggering levels. The proposed method is featured by the fact that the information from the signal is fully utilized using multiple triggering levels and the outliers are sifted out using consistency analysis, thus making the identified result more accurate and reliable. The effectiveness and accuracy of the method have been demonstrated in the examples using the simulated data and experimental data.

scholarly journals Modal parameters identification of bridge by improved stochastic subspace identification method with Grubbs criterion

2021 ◽  
Vol 54 (3-4) ◽  
pp. 457-464
Author(s):  
Yulin Zhou ◽  
Xulei Jiang ◽  
Mingjin Zhang ◽  
Jinxiang Zhang ◽  
Hao Sun ◽  
...  
Keyword(s):  
Wind Tunnel ◽  
Parameter Identification ◽  
Damping Ratio ◽  
Wind Tunnel Test ◽  
Modal Parameters ◽  
Subspace Identification ◽  
Identification Algorithm ◽  
Modal Parameter ◽  
Modal Parameter Identification ◽  
Stochastic Subspace Identification

In the wind tunnel test of a long-span bridge model, to ensure that the dynamic characteristics of the model can satisfy the test design requirements, it is particularly important to accurately identify the modal parameters of the model. First, the stochastic subspace identification algorithm was used to analyze the modal parameters of the model in the wind tunnel test; then, Grubbs criterion was introduced to effectively eliminate outliers in the damping ratio matrix. Stochastic subspace identification algorithm with Grubbs criterion improved the accuracy of the modal parameter identification and the ability to determine system matrix order and prevented the modal omissions caused by determining the stable condition of the damping ratio in the stability diagram. Finally, Oujiang Bridge was used as an example to verify the stochastic subspace identification algorithm with Grubbs criterion and compare with the results of the finite element method. The example shows that the improved method can be effectively applied to the modal parameter identification of bridges.

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