Cumulant Based Modal Parameter Extraction of Structures

1998 ◽  
Vol 120 (2) ◽  
pp. 378-383
Author(s):  
T. P. Runarsson ◽  
M. T. Jonsson ◽  
G. R. Jonsson

This paper describes a nonlinear deterministic estimator based on cumulants for the extraction of modal parameters. The signal analysed is composed of multiple exponentially damped real sinusoids in unknown additive noise. Cumulants reduce significantly the effects of noise and are also an efficient way of compressing the sampled data. In modal analysis a sensor may be unable to detect some modes of vibration due to its location. Cumulants estimated from real data sampled at different locations and instances are added directly together. This average cumulant function will contain the eigenvalues for all excited modes of vibration. Finding the frequencies and corresponding damping factors is therefore reduced to solving a single average cumulant function. The performance of the proposed estimator is examined and compared with the Eigensystem Realization Algorithm via simulations.

2019 ◽  
Vol 11 (2) ◽  
pp. 324-337
Author(s):  
Sk Abdul Kaium ◽  
Sayed Abul Hossain ◽  
Jafar Sadak Ali

Purpose The purpose of this paper is to highlight that the need for improved system identification methods within the domain of modal analysis increases under the impulse of the broadening field of applications, e.g., damage detection and vibro-acoustics, and the increased complexity of today’s structures. Although significant research efforts during the last two decades have resulted in an extensive number of parametric identification algorithms, most of them are certainly not directly applicable for modal parameter extraction. So, based on this, the aim of the present work is to develop a technique for modal parameter extraction from the measured signal. Design/methodology/approach A survey and classification of the different modal analysis methods are made; however, the focus of this thesis is placed on modal parameter extraction from measured time signal. Some of the methods are examined in detail, including both single-degree-of-freedom and multi-degree-of-freedom approaches using single and global frequency-response analysis concepts. The theory behind each of these various analysis methods is presented in depth, together with the development of computer programs, theoretical and experimental examples and discussion, in order to evaluate the capabilities of those methods. The problem of identifying properties of structures that possess close modes is treated in particular detail, as this is a difficult situation to handle and yet a very common one in many structures. It is essential to obtain a good model for the behavior of the structure in order to pursue various applications of experimental modal analysis (EMA), namely: updating of finite element models, structural modification, subsystem-coupling and calculation of real modes from complex modes, to name a few. This last topic is particularly important for the validation of finite element models, and for this reason, a number of different methods to calculate real modes from complex modes are presented and discussed in this paper. Findings In this paper, Modal parameters like mode shapes and natural frequencies are extracted using an FFT analyzer and with the help of ARTeMiS, and subsequently, an algorithm has been developed based on frequency domain decomposition (FDD) technique to check the accuracy of the results as obtained from ARTeMiS. It is observed that the frequency domain-based algorithm shows good agreement with the extracted results. Hence the following conclusion may be drawn: among several frequency domain-based algorithms for modal parameter extraction, the FDD technique is more reliable and it shows a very good agreement with the experimental results. Research limitations/implications In the case of extraction techniques using measured data in the frequency domain, it is reported that the model using derivatives of modal parameters performed better in many situations. Lack of accurate and repeatable dynamic response measurements on complex structures in a real-life situation is a challenging problem to analyze exact modal parameters. Practical implications During the last two decades, there has been a growing interest in the domain of modal analysis. Evolved from a simple technique for troubleshooting, modal analysis has become an established technique to analyze the dynamical behavior of complex mechanical structures. Important examples are found in the automotive (cars, trucks, motorcycles), railway, maritime, aerospace (aircrafts, satellites, space shuttle), civil (bridges, buildings, offshore platforms) and heavy equipment industry. Social implications Presently structural health monitoring has become a significantly important issue in the area of structural engineering particularly in the context of safety and future usefulness of a structure. A lot of research is being carried out in this area incorporating the modern sophisticated instrumentations and efficient numerical techniques. The dynamic approach is mostly employed to detect structural damage, due to its inherent advantage of having global and location-independent responses. EMA has been attempted by many researchers in a controlled laboratory environment. However, measuring input excitation force(s) seems to be very expensive and difficult for the health assessment of an existing real-life structure. So Ambient Vibration Analysis is a good alternative to overcome those difficulties associated with the measurement of input excitation force. Originality/value Three single bay two storey frame structure has been chosen for the experiment. The frame has been divided into six small elements. An algorithm has been developed to determine the natural frequency of those frame structures of which one is undamaged and the rest two damages in single element and double element, respectively. The experimental results from ARTeMIS and from developed algorithm have been compared to verify the effectiveness of the developed algorithm. Modal parameters like mode shapes and natural frequencies are extracted using an FFT analyzer and with the help of ARTeMiS, and subsequently, an algorithm has been programmed in MATLAB based on the FDD technique to check the accuracy of the results as obtained from ARTeMiS. Using singular value decomposition, the power Spectral density function matrix is decomposed using the MATLAB program. It is observed that the frequency domain-based algorithm shows good consistency with the extracted results.


2020 ◽  
Vol 12 (10) ◽  
pp. 168781402096832
Author(s):  
Xuchu Jiang ◽  
Xinyong Mao ◽  
Yingjie Chen ◽  
Caihua Hao

The states of the machine tool, such as the components’ position and the spindle speed, play leading roles in the change of dynamic parameters. However, the traditional modal analysis method that modal parameters manually identified from vibration signal is greatly interfered by harmonics, and the process of eliminating interference is very inefficient and subjective. At present, there is a lack of a standard and efficient method to characterize modal parameter changes in different states of machine tools. This paper proposes a new machine tool modal classification analysis method based on clustering. The characteristics related to the modal parameters are extracted from the response signal in different states, and the clustering results are used to reflect the changes of machine tool modal parameters. After the amplitude of the frequency response function is normalized, the characteristics related to the natural frequency are acquired, and the clustering results further reflect the difference of the natural frequency of the signal. The new method based on clustering can be a standard and efficient method to characterize modal parameter changes in different states of machine tools.


Author(s):  
Wenlong Yang ◽  
Lei Li ◽  
Qiang Fu ◽  
Yao Teng ◽  
Shuqing Wang ◽  
...  

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.


2011 ◽  
Vol 301-303 ◽  
pp. 629-634
Author(s):  
Yi Feng Xu ◽  
Jun Wang

The aim of this paper is to validate the modal parameters used in coupled structural finite element and acoustic boundary element algorithm to analysis the structure subjected to diffuse acoustic field. The theoretical deduction of non-symmetric coupled vibro-acoustical modal analysis was introduced firstly. In order to verify the modal truncation frequency how to affect the simulation results, based on the reciprocity theorem used in coupled FE-BE model, three different truncation frequency conditions were performed. The contrastive results show that twice the upper calculation frequency as the truncated modal frequency can make the simulation effectively and efficiently.


1988 ◽  
Vol 110 (1) ◽  
pp. 24-29 ◽  
Author(s):  
J.-N. Juang ◽  
H. Suzuki

This paper demonstrates the close conceptual relationships between time domain and frequency domain approaches to identification of modal parameters for linear systems. A frequency domain eigensystem realization algorithm, via transfer functions, is developed using a known procedure formulated for a time domain eigensystem realization algorithm, via free decay measurement data. An important feature is the capability of windowing to concentrate analysis on the frequency range of interest. The procedure of overlap averaging is used to produce smoother spectra to reduce the effect of noise on identified modal parameters. Examples from simulation and experiments are given to illustrate the validity of formulations derived in the paper.


2013 ◽  
Vol 744 ◽  
pp. 137-142
Author(s):  
Li Zhang ◽  
Su Bin ◽  
Feng Tao ◽  
Ye Tian ◽  
Jiang Yue Peng

In this paper, the free modal of the industrial flat sewing machine was researched, and the experimental measurement system is established. A modal analysis of industrial flat sewing machine is carried out through the method of single point exciting vibration and multipoint collecting signal. The PolyMax modal parameter identification method is applied to the modal analysis for frequency response function to get steady state diagram and then determine the modal parameters and modal shapes. According to Modal Assurance Criterion (MAC), the credibility of the calculation results is verified, and then modal parameters of industrial flat sewing machine get more reliable in order to provide the reference for further structure optimization and noise reduction of the flat sewing machine.


2021 ◽  
Vol 11 (23) ◽  
pp. 11432
Author(s):  
Xiangying Guo ◽  
Changkun Li ◽  
Zhong Luo ◽  
Dongxing Cao

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.


Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5664
Author(s):  
Jiqiao Zhang ◽  
Zhihua Wu ◽  
Gongfa Chen ◽  
Qiang Liang

This paper proposes a differential filtering method for the identification of modal parameters of bridges from unmanned aerial vehicle (UAV) measurement. The determination of the modal parameters of bridges is a key issue in bridge damage detection. Accelerometers and fixed cameras have disadvantages of deployment difficulty. Hence, the actual displacement of a bridge may be obtained by using the digital image correlation (DIC) technology from the images collected by a UAV. As drone movement introduces false displacement into the collected images, the homography transformation is commonly used to achieve geometric correction of the images and obtain the true displacement of the bridge. The homography transformation is not always applicable as it is based on at least four static reference points on the plane of target points. The proposed differential filtering method does not request any reference points and will greatly accelerate the identification of the modal parameters. The displacement of the points of interest is tracked by the DIC technology, and the obtained time history curves are processed by differential filtering. The filtered signals are input into the modal analysis system, and the basic modal parameters of the bridge model are obtained by the operational modal analysis (OMA) method. In this paper, the power spectral density (PSD) is used to identify the natural frequencies; the mode shapes are determined by the ratio of the PSD transmissibility (PSDT). The identification results of three types of signals are compared: UAV measurement with differential filtering, UAV measurement with homography transformation, and accelerometer-based measurement. It is found that the natural frequencies recognized by these three methods are almost the same. This paper demonstrates the feasibility of UAV-differential filtering method in obtaining the bridge modal parameters; the problems and challenges in UAV measurement are also discussed.


2007 ◽  
Vol 353-358 ◽  
pp. 1195-1198 ◽  
Author(s):  
Y.B. Chen ◽  
J.G. Han ◽  
D.Q. Yang

Structural operating conditions may significantly differ from those applied during laboratory tests where the structure is well known, well installed and properly excited. For structures under their natural loading conditions, or excited by random forces, excitations cannot be measured and are usually non stationary. Hence, an improvement operational modal analysis is a useful complement to the traditional modal analysis approach. The aim of this paper is to present the application of a new identification procedure, named wavelet-based identification technique of structural modal parameters. Wavelet-based identification that works in time-frequency domain is used to identify the dynamic characteristics of the structural system in terms of natural frequencies, damping coefficients and mode shapes. The paper has shown how the amplitude and the phase of the wavelet transform of operational vibration measurements are related to eigenfrequencies and damping coefficients, and the wavelet-based spectrum analysis is used to identify the mode shapes of the structure. Those modal parameters can be used to detect damage of structures. A simulation example has demonstrated that current identified results are comparable with those previously obtained from the peak pick method in frequency domain and stochastic subspace identification in time domain.


2019 ◽  
Vol 255 ◽  
pp. 01004
Author(s):  
M. Danial A. Hasan ◽  
Z. A. B. Ahmad ◽  
M. Salman Leong ◽  
L. M. Hee

The present paper deals with the novel approach of filtering technique using hybrid of empirical mode decomposition technique with stabilization diagram, that autonomously implemented within Matlab. Noise or unwanted signal is always present in the data and a bad signal-to-noise can cause a severe error in modal parameter extraction. With the recent developments of automated procedures without user interaction for the operational modal analysis (OMA), the corrupted input signals turn out to be a big issue in obtaining reliable results of automated modal parameter identification. The appearance of noise or unwanted modes due to environmental effects could affect the actual structural modes selection. There is a significant issue regarding “noise” (or spurious) modes and eliminating them from the raw signal remains to be solved and requires a lot of interaction with an expert user. In the parametric modal analysis, oversizing of a modal model is usually performed to minimize the bias on modal estimates by getting all physical modes in the frequency range of interest and help to obtain a good model fit to the data. However, this will introduce noise modes. Thus, authors take advantage of tools, such as the stabilization diagram, to illustrate, and decide, if a mode is physical or not. This selection is not a trivial task, but it may be difficult and time consuming depending on the quality of data, the performance of the estimator and the experience of the user. Since the extensive interaction between tools and user is inappropriate for monitoring purposes, image clustering tool is introduced to separate the physical poles from the others with short response time and low computational efforts compared to the available clustering algorithm. Meanwhile, Empirical mode decomposition (EMD) is then introduced to break down a signal into various components without leaving the time domain and purposely used for filtering. These are a great combination as well as an effective procedure in producing a good input signal that free from unwanted modes which can cause disruptive decision making for the actual modes selection.


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