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.

scholarly journals Automated Modal Analysis for Tracking Structural Change during Construction and Operation Phases

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 927 ◽  
Author(s):  
Jun Teng ◽  
De-Hui Tang ◽  
Xiao Zhang ◽  
Wei-Hua Hu ◽  
Samir Said ◽  
...  
Keyword(s):  
Modal Analysis ◽  
Structural Changes ◽  
Spatial Clustering ◽  
Dynamic Monitoring ◽  
Model Order ◽  
Operational Conditions ◽  
Stabilization Diagram ◽  
Scale Experiment ◽  
Artificial Neural Network Ann ◽  
Continuous Dynamic

The automated modal analysis (AMA) technique has attracted significant interest over the last few years, because it can track variations in modal parameters and has the potential to detect structural changes. In this paper, an improved density-based spatial clustering of applications with noise (DBSCAN) is introduced to clean the abnormal poles in a stabilization diagram. Moreover, the optimal system model order is also discussed to obtain more stable poles. A numerical simulation and a full-scale experiment of an arch bridge are carried out to validate the effectiveness of the proposed algorithm. Subsequently, the continuous dynamic monitoring system of the bridge and the proposed algorithm are implemented to track the structural changes during the construction phase. Finally, the artificial neural network (ANN) is used to remove the temperature effect on modal frequencies so that a health index can be constructed under operational conditions.

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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