scholarly journals Cluster Analysis for Automated Operational Modal Analysis: A Review

2019 ◽  
Vol 255 ◽  
pp. 02012 ◽  
Author(s):  
M. Danial A. Hasan ◽  
Z. A. B. Ahmad ◽  
M. Salman Leong ◽  
L. M. Hee ◽  
M. Haffizzi Md. Idris
Keyword(s):  
Cluster Analysis ◽  
Modal Analysis ◽  
Clustering Algorithm ◽  
Operational Modal Analysis ◽  
Modal Identification ◽  
Modal Parameter ◽  
Essential Information ◽  
Clustering Techniques ◽  
Stabilization Diagram ◽  
Recent Developments

Recent developments in the field of modal-based damage detection and vibration-based monitoring have led to a renewed interest in automated procedures for the operational modal analysis (OMA). The development of automated operational modal analysis (OMA) procedures marked a fundamental step towards the elimination of any user intervention since traditional modal identification requires a lot of interaction by an expert user. A key for effective automation of OMA is depended on well- defined modal indicators for a clear indication about which modes are to be selected as the physical modes. In all modal analysis, the construction of stabilization diagrams is necessary in order to illustrate, and decide, if a mode is physical or not for predefined range of the model order. On the other hand, the use of stabilization diagram tools involves a large amount of user interaction, costly, time-consuming process and certainly unsuited for online applications. Therefore, the development of automatic procedures for the analysis of stabilization diagrams by resembling decision-making process of a human has been carried out in recent years. For the sake of clearness, the automation of the interpretation of stabilization diagrams can generally be divided into two steps in order to speed up the process: a) elimination of noise modes and b) clustering of physical modes in order to obtain the most representative values of the estimated parameters of each clustered mode. In recent years, several alternative procedures have been proposed for clustering techniques. Therefore, this review aims to provide relevant essential information on the recent developments of cluster analysis in automated OMA. A literature review of existing clustering algorithm has been carried out to find best practice criteria for automated modal parameter identification which involving the general concepts of these techniques as well as the pro and cons of applying these clustering techniques are also discussed and summarised.

scholarly journals Operational Modal Analysis Based on Subspace Algorithm with an Improved Stabilization Diagram Method

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Shiqiang Qin ◽  
Juntao Kang ◽  
Qiuping Wang
Keyword(s):  
Modal Analysis ◽  
Confidence Intervals ◽  
Singular Values ◽  
Operational Modal Analysis ◽  
Modal Parameter ◽  
Model Order Selection ◽  
Model Order ◽  
Diagram Method ◽  
Stochastic Subspace Identification ◽  
Stabilization Diagram

Subspace-based algorithms for operational modal analysis have been extensively studied in the past decades. In the postprocessing of subspace-based algorithms, the stabilization diagram is often used to determine modal parameters. In this paper, an improved stabilization diagram is proposed for stochastic subspace identification. Specifically, first, a model order selection method based on singular entropy theory is proposed. The singular entropy increment is calculated from nonzero singular values of the output covariance matrix. The corresponding model order can be selected when the variation of singular entropy increment approaches to zero. Then, the stabilization diagram with confidence intervals which is established using the uncertainty of modal parameter is presented. Finally, a simulation example of a four-story structure and a full-scale cable-stayed footbridge application is employed to illustrate the improved stabilization diagram method. The study demonstrates that the model order can be reasonably determined by the proposed method. The stabilization diagram with confidence intervals can effectively remove the spurious modes.

scholarly journals Automated Denoising Technique for Random Input Signals using Empirical Mode Decomposition (EMD)-Stabilization Diagram

2019 ◽  
Vol 255 ◽  
pp. 01004
Author(s):  
M. Danial A. Hasan ◽  
Z. A. B. Ahmad ◽  
M. Salman Leong ◽  
L. M. Hee
Keyword(s):  
Modal Analysis ◽  
Empirical Mode Decomposition ◽  
Clustering Algorithm ◽  
Parameter Extraction ◽  
Image Clustering ◽  
Quality Of Data ◽  
Modal Parameter ◽  
Mode Decomposition ◽  
Stabilization Diagram ◽  
Input Signals

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.

scholarly journals Full-Field Operational Modal Analysis of an Aircraft Composite Panel from the Dynamic Response in Multi-Impact Test

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1602
Author(s):  
Ángel Molina-Viedma ◽  
Elías López-Alba ◽  
Luis Felipe-Sesé ◽  
Francisco Díaz
Keyword(s):  
Modal Analysis ◽  
Energy Absorption ◽  
High Speed ◽  
Operational Modal Analysis ◽  
Modal Identification ◽  
Image Correlation ◽  
Composite Panel ◽  
Impact Excitation ◽  
Full Field ◽  
The Impact

Experimental characterization and validation of skin components in aircraft entails multiple evaluations (structural, aerodynamic, acoustic, etc.) and expensive campaigns. They require different rigs and equipment to perform the necessary tests. Two of the main dynamic characterizations include the energy absorption under impact forcing and the identification of modal parameters through the vibration response under any broadband excitation, which also includes impacts. This work exploits the response of a stiffened aircraft composite panel submitted to a multi-impact excitation, which is intended for impact and energy absorption analysis. Based on the high stiffness of composite materials, the study worked under the assumption that the global response to the multi-impact excitation is linear with small strains, neglecting the nonlinear behavior produced by local damage generation. Then, modal identification could be performed. The vibration after the impact was measured by high-speed 3D digital image correlation and employed for full-field operational modal analysis. Multiple modes were characterized in a wide spectrum, exploiting the advantages of the full-field noninvasive techniques. These results described a consistent modal behavior of the panel along with good indicators of mode separation given by the auto modal assurance criterion (Auto-MAC). Hence, it illustrates the possibility of performing these dynamic characterizations in a single test, offering additional information while reducing time and investment during the validation of these structures.

scholarly journals Looseness localization for bolted joints using Bayesian operational modal analysis and modal strain energy

2018 ◽  
Vol 10 (11) ◽  
pp. 168781401880869 ◽  
Author(s):  
Yu-Jia Hu ◽  
Wei-Gong Guo ◽  
Cheng Jiang ◽  
Yun-Lai Zhou ◽  
Weidong Zhu
Keyword(s):  
Modal Analysis ◽  
Strain Energy ◽  
Damage Index ◽  
Operational Modal Analysis ◽  
Modal Identification ◽  
Bolted Joints ◽  
Mode Shapes ◽  
Bolted Connections ◽  
Modal Strain Energy ◽  
Beam Structures

Bayesian operational modal analysis and modal strain energy are employed for determining the damage and looseness of bolted joints in beam structures under ambient excitation. With this ambient modal identification technique, mode shapes of a damaged beam structure with loosened bolted connections are obtained based on Bayesian theory. Then, the corresponding modal strain energy can be calculated based on the mode shapes. The modal strain energy of the structure with loosened bolted connections is compared with the theoretical one without bolted joints to define a damage index. This approach uses vibration-based nondestructive testing of locations and looseness of bolted joints in beam structures with different boundary conditions by first obtaining modal parameters from ambient vibration data. The damage index is then used to identify locations and looseness of bolted joints in beam structures with single or multiple bolted joints. Furthermore, the comparison between damage indexes due to different looseness levels of bolted connections demonstrates a qualitatively proportional relationship.

scholarly journals A new automated procedure of modal identification in operational conditions

2018 ◽  
Vol 211 ◽  
pp. 21003 ◽  
Author(s):  
Gabriele Marrongelli ◽  
Carmelo Gentile
Keyword(s):  
Parametric Identification ◽  
Modal Identification ◽  
Mode Shapes ◽  
Parameter Estimates ◽  
Reference List ◽  
Modal Parameter ◽  
Operational Conditions ◽  
Historic Masonry ◽  
Stabilization Diagram ◽  
Using Data

Structural Health Monitoring (SHM) strategies are aimed at the assessment of structural performance, using data acquired by sensing systems. Among the different available approaches, vibration-based methods - involving the automation of the modal parameter estimation (MPE) and modal tracking (MT) procedures - are receiving increasing attention. In the context of vibration-based monitoring, this paper presents an automated procedure of modal identification in operational conditions. The presented algorithms can be used to effectively manage the results obtained by any parametric identification method that involves the construction and the interpretation of stabilization diagrams. The implemented approach introduces improvements related to both the MPE and the MT tasks. The MPE procedure consists of three key steps aimed at: (1) filtering a high number of spurious poles in the stabilization diagram; (2) clustering the remaining poles that share same characteristics in term of modal parameters; (3) improving the accuracy of the modal parameter estimates. In the MT procedure the use of a simple statistical approach to define adaptive thresholds together with continuously updated dynamic reference list guarantee an efficient tracking of the most representative structural modes. The advantages obtained through the proposed procedures are exemplified using data continuously collected on the historic masonry tower of San Gottardo in Corte, located in the centre of Milan, Italy. In addition, the ability of the automated algorithms to identify contributions inherent to different vibration modes, even if they are characterized by closely-spaced frequencies and a low discriminant between mode shapes, will be described in details.

Towards an automatic spectral and modal identification from operational modal analysis

2013 ◽  
Vol 332 (1) ◽  
pp. 213-227 ◽  
Author(s):  
V.H. Vu ◽  
M. Thomas ◽  
F. Lafleur ◽  
L. Marcouiller
Keyword(s):  
Modal Analysis ◽  
Operational Modal Analysis ◽  
Modal Identification

Chatter prediction based on operational modal analysis and an adaptive complex Morlet filter

2020 ◽  
Vol 62 (8) ◽  
pp. 484-492
Author(s):  
Kai Yang ◽  
Guofeng Wang ◽  
Kaile Ma
Keyword(s):  
Modal Analysis ◽  
Machine Tools ◽  
Production Efficiency ◽  
Stability Boundary ◽  
Operational Modal Analysis ◽  
Modal Parameter ◽  
Local Character ◽  
Specific Frequency ◽  
Harmonic Components ◽  
The Stability

Chatter that occurs between a cutting tool and a workpiece greatly reduces the surface quality and production efficiency. Therefore, it is of great importance to predict and avoid chatter so as to guarantee the stability of the manufacturing process. To realise the accurate prediction of the stability boundary of machine tools, operational modal analysis (OMA) is increasingly receiving attention due to its adequate consideration of variations in working conditions in the industrial environment. However, because of the influence of harmonic components in the response signals, the accuracy in identifying the modal parameters is seriously compromised. In this paper, an adaptive complex Morlet filter (ACMF) is presented to remove the harmonic components by adaptively adjusting the centre frequency and bandwidth according to the local character of the ambient environment in a specific frequency range and filtering out harmonic components that are not strict integer multiples of the fundamental frequency owing to non-rigid periodic motion of the machine tool spindle. In order to show the effectiveness of the proposed method, milling experiments are carried out and experimental modal analysis (EMA) is utilised to make comparisons with the proposed method. Moreover, comparisons between the ACMF and two other typical filtering methods are made. The results indicate that the proposed method performs well in modal parameter recognition for machine tools.

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