@inproceedings{20183005588175 ,
language = {English},
copyright = {Compilation and indexing terms, Copyright 2023 Elsevier Inc.},
copyright = {Compendex},
title = {Development of new technique in multi setup merging of vibration test system identification},
journal = {SHMII 2017 - 8th International Conference on Structural Health Monitoring of Intelligent Infrastructure, Proceedings},
author = {Sabamehr, A. and Mirshafiei, F. and Bagchi, A.},
year = {2017},
pages = {238 - 243},
address = {Brisbane, QLD, Australia},
abstract = {In Modal Analysis, multi setup merging is one of the high demand methods when the number of sensors is less than DOFs of structure. The technique is worked based on the selection reference sensors which is fixed during all measurements, and additional sensors are moved or roved in each setup to cover all areas of the structure. Generally, there are three common methods are used for merging multi-setup data including, Post Separate Estimation Re-scaling (PoSER), Post Global Estimation Re-scaling (PoGER) and Pre Global Estimation Rescaling (PreGER). Due to some difficulties in PoSER and PoGER, PreGER have been used recently as an alternative method because the merging process has been conducted before parameter estimation. On the other hand, Random decrement Technique is a time domain procedure which can be used for Operational Modal Analysis test. As OMA can be generated by zero mean Gaussian noise, the noise is removable by applying RDT on the period of time segment with specific trigger which keeps all of frequencies of structure but remove its noise. Therefore, RDT can apply to PreGER to enhance the speed and reduce the data in multi setup merging method. In this paper, Finite Element (FE) model of five-story frame has been tested with multi setup merging. Consequently, the results shows that applying RDT as a pre-process technique to the data can enhance the performance of the system identification process, and confine the number of data in PreGER.<br/> &copy; 2017 International Society for Structural Health  Monitoring of Intelligent Infrastrucure. All rights reserved.},
key = {Merging},
keywords = {Time domain analysis;Modal analysis;Structural health monitoring;Religious buildings;Gaussian noise (electronic);},
note = {Global estimation;Merging process;Number of datum;Operational modal analysis;Process techniques;Random decrement technique;Reference sensors;Vibration test system;},
}

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The technique is worked based on the selection reference sensors which is fixed during all measurements, and additional sensors are moved or roved in each setup to cover all areas of the structure. Generally, there are three common methods are used for merging multi-setup data including, Post Separate Estimation Re-scaling (PoSER), Post Global Estimation Re-scaling (PoGER) and Pre Global Estimation Rescaling (PreGER). Due to some difficulties in PoSER and PoGER, PreGER have been used recently as an alternative method because the merging process has been conducted before parameter estimation. On the other hand, Random decrement Technique is a time domain procedure which can be used for Operational Modal Analysis test. As OMA can be generated by zero mean Gaussian noise, the noise is removable by applying RDT on the period of time segment with specific trigger which keeps all of frequencies of structure but remove its noise. Therefore, RDT can apply to PreGER to enhance the speed and reduce the data in multi setup merging method. In this paper, Finite Element (FE) model of five-story frame has been tested with multi setup merging. Consequently, the results shows that applying RDT as a pre-process technique to the data can enhance the performance of the system identification process, and confine the number of data in PreGER.<br/> &copy; 2017 International Society for Structural Health Monitoring of Intelligent Infrastrucure. All rights reserved.","key":"20183005588175","keywords":"Time domain analysis;Modal analysis;Structural health monitoring;Religious buildings;Gaussian noise (electronic);","note":"Global estimation;Merging process;Number of datum;Operational modal analysis;Process techniques;Random decrement technique;Reference sensors;Vibration test system;","bibtex":"@inproceedings{20183005588175 ,\nlanguage = {English},\ncopyright = {Compilation and indexing terms, Copyright 2023 Elsevier Inc.},\ncopyright = {Compendex},\ntitle = {Development of new technique in multi setup merging of vibration test system identification},\njournal = {SHMII 2017 - 8th International Conference on Structural Health Monitoring of Intelligent Infrastructure, Proceedings},\nauthor = {Sabamehr, A. and Mirshafiei, F. and Bagchi, A.},\nyear = {2017},\npages = {238 - 243},\naddress = {Brisbane, QLD, Australia},\nabstract = {In Modal Analysis, multi setup merging is one of the high demand methods when the number of sensors is less than DOFs of structure. The technique is worked based on the selection reference sensors which is fixed during all measurements, and additional sensors are moved or roved in each setup to cover all areas of the structure. Generally, there are three common methods are used for merging multi-setup data including, Post Separate Estimation Re-scaling (PoSER), Post Global Estimation Re-scaling (PoGER) and Pre Global Estimation Rescaling (PreGER). Due to some difficulties in PoSER and PoGER, PreGER have been used recently as an alternative method because the merging process has been conducted before parameter estimation. On the other hand, Random decrement Technique is a time domain procedure which can be used for Operational Modal Analysis test. As OMA can be generated by zero mean Gaussian noise, the noise is removable by applying RDT on the period of time segment with specific trigger which keeps all of frequencies of structure but remove its noise. Therefore, RDT can apply to PreGER to enhance the speed and reduce the data in multi setup merging method. In this paper, Finite Element (FE) model of five-story frame has been tested with multi setup merging. Consequently, the results shows that applying RDT as a pre-process technique to the data can enhance the performance of the system identification process, and confine the number of data in PreGER.<br/> &copy; 2017 International Society for Structural Health Monitoring of Intelligent Infrastrucure. 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