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

Qin, Shiqiang; Kang, Juntao; Wang, Qiuping

doi:10.1155/2016/7598965

Cited by 19 publications

(13 citation statements)

References 27 publications

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“…In recent years, many studies attempted to automate the interpretation of stabilization diagram or the modal parameter estimation algorithm in general [13][14][15][16][17]. Owing to the fact that analyzing the stabilization diagram reduces to finding modes with similar properties, the majority of automation strategies borrow methods from statistical machine learning with supervised and unsupervised learning algorithms.…”

Section: Pertinent Literaturementioning

confidence: 99%

Automated modal parameter estimation using correlation analysis and bootstrap sampling

Yaghoubi

Vakilzadeh

Abrahamsson

2018

Mechanical Systems and Signal Processing

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The estimation of modal parameters from a set of noisy measured data is a highly judgmental task, with user expertise playing a significant role in distinguishing between estimated physical and noise modes of a test-piece. Various methods have been developed to automate this procedure. The common approach is to identify models with different orders and cluster similar modes together. However, most proposed methods based on this approach suffer from high-dimensional optimization problems in either the estimation or clustering step. To overcome this problem, this study presents an algorithm for autonomous modal parameter estimation in which the only required optimization is performed in a three-dimensional space. To this end, a subspace-based identification method is employed for the estimation and a non-iterative correlation-based method is used for the clustering. This clustering is at the heart of the paper. The keys to success are correlation metrics that are able to treat the problems of spatial eigenvector aliasing and nonunique eigenvectors of coalescent modes simultaneously. The algorithm commences by the identification of an excessively high-order model from frequency response function test data. The high number of modes of this model provide bases for two subspaces: one for likely physical modes of the tested system and one for its complement dubbed the subspace of noise modes. By employing the bootstrap resampling technique, several subsets are generated from the same basic dataset and for each of them a model is identified to form a set of models. Then, by correlation analysis with the two aforementioned subspaces, highly correlated modes of these models which appear repeatedly are clustered together and the noise modes are collected in a so-called Trashbox cluster. Stray noise modes attracted to the mode clusters are trimmed away in a second step by correlation analysis. The final step of the algorithm is a fuzzy c-means clustering procedure applied to a three-dimensional feature space to assign a degree of physicalness to each cluster. The proposed algorithm is applied to two case studies: one with synthetic data and one with real test data obtained from a hammer impact test. The results indicate that the algorithm successfully clusters similar modes and gives a reasonable quantification of the extent to which each cluster is physical.

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Section: Pertinent Literaturementioning

confidence: 99%

Automated modal parameter estimation using correlation analysis and bootstrap sampling

Yaghoubi

Vakilzadeh

Abrahamsson

2018

Mechanical Systems and Signal Processing

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“…Most automatic SD is based on hierarchal clustering, which group similar poles on the basis of their properties corresponding to stability criteria. Subspace identification (SSI) method is one of the most popular OMA method used in the automated OMA . Bakir applied modal phase collinearity criterion to measure the spatial stability of identified poles from SD, which is modeled as histogram composed of overlapping bins .…”

Section: Introductionmentioning

confidence: 99%

“…Mrabet et al presented a criterion on the basis of the calculation of the modal assurance criterion (MAC) between two identified covariance vectors in SSI method . Qin et al introduced a method basis on singular entropy theory to model order selection in SSI methods and used the uncertainty of modal parameters identification to establish an SD with confidence interval . Reynders established an automated SD construction procedure that first separates all poles in two categories by using a partitioning method.…”

Section: Introductionmentioning

confidence: 99%

Enhanced stabilization diagram for automated modal parameter identification based on power spectral density transmissibility functions

Khodaygan

2019

Struct Control Health Monit

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Operational modal analysis based on power spectral density transmissibility (PSDT) functions is a useful tool to identify the modal parameters with low sensitivity to excitations. For pole extraction from the PSDT function, a proper parametric identification method such as the polyreference least squares complex frequency-domain method or poly-Max method can be used. Then, the poles are selected from a stabilization diagram (SD) with overestimating the system model order. Therefore, spurious modes can be identified that must be distinguished and removed from the system poles. To reach this aim, many techniques have been proposed and applied. In this paper, a new algorithm is proposed to enhance the performance of the SD for automated modal parameter identification based on the PSDT. The algorithm is composed of two main phases. In the first phase, the spurious modes are discriminated from the system poles on the basis of the conventional and supplementary stability criteria. On spurious mode omission, two new criteria named "pole criterion" and "coherence criterion" are introduced and applied as the supplementary stability criteria to make a more clear SD. Then, the extracted poles are categorized in the distinct clusters through a new strategy for comparing modes. In the second phase, a novel multiscreening algorithm is implemented for the automated identification of the system poles. Accordingly, the searching and averaging processes are followed between clusters, and the poles are screened to automatically identify the system poles on the basis of the numbers of their repetition in the SD via k-means clustering algorithms. Also, to improve the accuracy of the identification, the Hilbert transform is used in the construction of the PSDT functions. Finally, to validate and demonstrate the efficiency of the proposed method, a computer simulation and an experiential case study are considered.

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“…Modal analysis is widely used in many fields such as architecture, bridges, and vehicles to extract the vibration modes of structures [13][14][15][16][17][18][19][20]. Experimental modal testing is an important type of modal analysis and has rapidly developed in recent years.…”

Section: Introductionmentioning

confidence: 99%

Numerical Simulation and Experimental Validation of the Vibration Modes for a Processing Reciprocating Compressor

Zhao

Chen

Zhou

et al. 2017

Shock and Vibration

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The low-order vibration modes of a reciprocating compressor were studied by means of numerical simulation and experimental validation. A shell element model, a beam element model, and two solid element models were established to investigate the effects of bolted joints and element types on low-order vibration modes of the compressor. Three typical cases were compared to check the effect of locations of moving parts on the vibration modes of the compressor. A forced modal test with the MRIT (Multiple References Impact Test) technique was conducted to validate the simulation results. Among four numerical models, the solid element model with the bolt-pretension method showed the best accuracy compared with experimental data but the worst computational efficiency. The shell element model is recommended to predict the low-order vibration modes of the compressor with regard to effectiveness and usefulness. The sparsely distributed bolted joints with a small bonded region on the contact surface were key bolted joints that had greater impacts on the low-order vibration modes of the compressor than the densely distributed bolted joints. The positions of the moving parts had little effect on the low-order vibration modes of the compressor.

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

Cited by 19 publications

References 27 publications

Automated modal parameter estimation using correlation analysis and bootstrap sampling

Automated modal parameter estimation using correlation analysis and bootstrap sampling

Enhanced stabilization diagram for automated modal parameter identification based on power spectral density transmissibility functions

Numerical Simulation and Experimental Validation of the Vibration Modes for a Processing Reciprocating Compressor

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