Modal Identification from Nonstationary Ambient Vibration Data Using Random Decrement Algorithm

2012 ◽  
pp. 223-232
Author(s):  
Chang-Sheng Lin ◽  
Dar-Yun Chiang
Keyword(s):  
Modal Identification ◽  
Ambient Vibration ◽  
Vibration Data ◽  
Random Decrement ◽  
Nonstationary Ambient Vibration

scholarly journals Automated band selection for Bayesian FFT modal identification Based on RetinaNet

2021 ◽  
Vol 2078 (1) ◽  
pp. 012058
Author(s):  
Chen Wang ◽  
Zhilin Xue ◽  
Yipeng Su ◽  
Binbin Li
Keyword(s):  
Operation Time ◽  
Modal Identification ◽  
Ambient Vibration ◽  
Mode Shapes ◽  
Band Selection ◽  
Data Sets ◽  
Civil Structures ◽  
Frequency Bands ◽  
Operational Conditions ◽  
Vibration Data

Abstract Bayesian FFT algorithm is a popular method to identify modal parameters, e.g., modal frequencies, damping ratios, and mode shapes, of civil structures under operational conditions. It is efficient and provides the identification uncertainty in terms of posterior distribution. However, in utilizing the Bayesian FFT algorithm, it is tedious to manually select frequency bands and initial frequencies. This step requires professional knowledge and costs most of time, which prevents the automation of Bayesian FFT algorithm. Regarding the band selection as an object detection problem, we design a band selection network based on the RetinaNet to automatically select frequency bands and a peak prediction network to predict the initial frequencies. The designed networks are trained using the singular value spectrum of measured ambient vibration data and verified by various data sets. It can achieve the human accuracy with much less operation time, and thus provides a corner stone for the automation of Bayesian FFT algorithm.

Extending Blind Modal Identification to the Underdetermined Case for Ambient Vibration

2012 ◽  
Author(s):  
Scot McNeill
Keyword(s):  
Natural Frequencies ◽  
Second Order ◽  
Modal Identification ◽  
Ambient Vibration ◽  
Mode Shapes ◽  
Frequency Domain Method ◽  
Blind Identification ◽  
Vibration Data ◽  
Modal Damping ◽  
Six Dof

The modal identification framework known as Blind Modal Identification (BMID) has recently been developed, drawing on techniques from Blind Source Separation (BSS). Therein, a BSS algorithm known as Second Order Blind Identification (SOBI) was adapted to solve the Modal IDentification (MID) problem. One of the drawbacks of the technique is that the number of modes identified must be less than the number of sensors used to measure the vibration of the equipment or structure. In this paper, an extension of the BMID method is presented for the underdetermined case, where the number of sensors is less than the number of modes to be identified. The analytic signal formed from measured vibration data is formed and the Second Order Blind Identification of Underdetermined Mixtures (SOBIUM) algorithm is applied to estimate the complex-valued modes and modal response autocorrelation functions. The natural frequencies and modal damping ratios are then estimated from the corresponding modal auto spectral density functions using a simple Single Degree Of Freedom (SDOF), frequency-domain method. Theoretical limitations on the number of modes identified given the number of sensors are provided. The method is demonstrated using a simulated six DOF mass-spring-dashpot system excited by white noise, where displacement at four of the six DOF is measured. All six modes are successfully identified using data from only four sensors. The method is also applied to a more realistic simulation of ambient building vibration. Seven modes in the bandwidth of interest are successfully identified using acceleration data from only five DOF. In both examples, the identified modal parameters (natural frequencies, mode shapes, modal damping ratios) are compared to the analytical parameters and are demonstrated to be of good quality.

scholarly journals DETERMINATION OF THE EFFECT OF TiO2 ON THE DYNAMIC BEHAVIOR OF SCALED CONCRETE CHIMNEY BY OMA

2021 ◽  
Vol 55 (3) ◽  
Author(s):  
Sertaç Tuhta ◽  
Furkan Günday
Keyword(s):  
Titanium Dioxide ◽  
Outer Surface ◽  
Ground Level ◽  
Modal Identification ◽  
Ambient Vibration ◽  
Mode Shapes ◽  
Dynamic Parameters ◽  
Vibration Data ◽  
Output Only ◽  
Frequency Domain Decomposition

In this article, the dynamic parameters (frequencies, mode shapes, damping ratios) of a scaled concrete chimney and the dynamic parameters (frequencies, mode shapes, damping ratios) of the entire outer surface of the 80-micron-thick titanium dioxide are compared using the operational modal analysis method. Ambient excitation was provided from micro tremor ambient vibration data at ground level. Enhanced Frequency Domain Decomposition (EFDD) is used for the output-only modal identification. From this study, very best correlation is found between the mode shapes. Titanium dioxide applied to the entire outer surface of the scaled concrete chimney has an average of 16.34 % difference in frequency values and 9.81 % in damping ratios, proving that nanomaterials can be used to increase the rigidity in chimneys, in other words, for reinforcement. Another important result determined in the study is that it has been observed that the adherence of titanium dioxide and similar nanomaterials mentioned in the introduction to concrete chimney surfaces is at the highest level.

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 A novel identification procedure from ambient vibration data

Meccanica ◽  
2020 ◽  
Author(s):  
A. Di. Matteo ◽  
C. Masnata ◽  
S. Russotto ◽  
C. Bilello ◽  
A. Pirrotta
Keyword(s):  
Hilbert Transform ◽  
Time Domain ◽  
Operating Conditions ◽  
Modal Identification ◽  
Ambient Vibration ◽  
System Response ◽  
The Real ◽  
Vibration Data ◽  
Identification Method ◽  
Time Domain Signal

AbstractAmbient vibration modal identification, also known as Operational Modal Analysis, aims to identify the modal properties of a structure based on vibration data collected when the structure is under its operating conditions, i.e., no initial excitation or known artificial excitation. This procedure for testing and/or monitoring historic buildings, is particularly attractive for civil engineers concerned with the safety of complex historic structures. However, since the external force is not recorded, the identification methods have to be more sophisticated and based on stochastic mechanics. In this context, this contribution will introduce an innovative ambient identification method based on applying the Hilbert Transform, to obtain the analytical representation of the system response in terms of the correlation function. In particular, it is worth stressing that the analytical signal is a complex representation of a time domain signal: the real part is the time domain signal itself, while the imaginary part is its Hilbert transform. A 3DOF numerical example will be presented to show the accuracy of the proposed procedure, and comparisons with data from other methods assess the reliability of the approach. Finally, the identification method will be extended to the real case study of the Chiaramonte Palace, a historic building located in Palermo and known as “Steri”.

Sign in / Sign up

Export Citation Format

Share Document