31 March 2010 Data filtering for robust modal identification using SOD and DSPI
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Modal analysis is a well developed field with many applications. The multi-output approaches in particular are well suited for system identification and online damage detection because they use the natural excitations the system undergoes in its normal operation. In this work two multi-output approaches are analyzed, compared, and improved upon. The first method is smooth orthogonal decomposition (SOD). SOD was originally developed as a tool for detecting features of chaotic dynamical systems. Recently, it has been used as a time-based multioutput modal analysis approach. SOD has been demonstrated effectively for the free vibration case and for random excitations. The second method is direct system parameter identification (DSPI). DSPI was developed as a time-based multi-input multi-output modal analysis approach. When the inputs are not measured DSPI can handle the free vibration case and random excitations like SOD. If the inputs are measured then DSPI works with arbitrary excitations. In addition to comparing SOD and DSPI, novel filtering algorithms are introduced to improve each method's performance when working with noisy data. Numerical simulations are carried out to compare the two methods and demonstrate the effectiveness of the filtering algorithms in improving frequency and mode shape extraction.
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Kiran D'Souza and Bogdan I. Epureanu "Data filtering for robust modal identification using SOD and DSPI", Proc. SPIE 7647, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2010, 76470M (31 March 2010); doi: 10.1117/12.847374; https://doi.org/10.1117/12.847374

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