Modal parameter based damage detection in operational modal analysis

2014 ◽  
pp. 1879-1884
Author(s):  
L Balsamo ◽  
S Mukhopadhyay ◽  
R Betti ◽  
H Lu_
Keyword(s):  
Damage Detection ◽  
Modal Analysis ◽  
Operational Modal Analysis ◽  
Modal Parameter

EFFECTIVENESS OF OPERATIONAL MODAL ANALYSIS (OMA) FOR DAMAGE DETECTION IN FIBERGLASS REINFORCED EPOXY

2015 ◽  
Vol 76 (8) ◽  
Author(s):  
Haizuan Abd Rahman ◽  
Ahmad Azlan Mat Isa ◽  
Abdul Rahim Bahari
Keyword(s):  
Finite Element Method ◽  
Boundary Condition ◽  
Composite Material ◽  
Damage Detection ◽  
Modal Analysis ◽  
Detection Method ◽  
Small Deviation ◽  
Operational Modal Analysis ◽  
Modal Parameters ◽  
Fiber Glass

This study attempts to apply vibration-based damage detection method specifically Operational Modal Analysis (OMA) on fiberglass reinforced epoxy plate. OMA is used on healthy fiber glass reinforced epoxy plate to extract the modal parameters and the procedure is extended to damaged fiberglass reinforced epoxy plate. Both healthy and damaged composite material are tested under different boundary conditions i.e. free-free on 4 edges, 1 edge clamped, 2 edges clamped, 3 edges clamped and 4 edges of free-free boundary condition. The result of frequency from OMA was compared analytically with Finite Element Method (FEM). Nastran software is employed in this study. The FEM using Nastran shows that the result obtained is not accurate enough compared to OMA. Therefore, another method was applied to look at the effectiveness of OMA method using Experimental Modal Analysis (EMA). It was observed that both EMA and OMA methods gave small deviation and good correlation.

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.

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 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 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.

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