F. Trainotti, S. W. B. Klaassen, T. Bregar, D. J. Rixen
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PRANK: A Singular Value Based Noise Filtering of Multiple Response Datasets for Experimental Dynamics
High quality measurements are paramount to a successful application of experimental techniques in structural dynamics. The presence of noise and disturbances can significantly distort the information stored in the data and, if not adequately treated, may result in erroneous findings and misleading predictions. A common technique to filter out noise relies on decomposing the dataset into singular components sorted by their degree of significance. Discarding low-value contributions helps to clean the data and remove spuriousness. This paper presents PRANK, a novel singular value-based reconstruction approach for multiple-response vibration datasets. PRANK integrates the effect of Principal Response Functions and Hankel filtering actions, resulting in an improved data reconstruction for both system poles and zeros. The proposed formulation is tested on both analytical and numerical examples, showcasing its robustness, efficiency and versatility. PRANK operates with both time- and frequency-based data. Applied to noisy full-field camera measurements, the filter delivered excellent performance, indicating its potential for various identification tasks and applications in vibration analysis.
期刊介绍:
Experimental Techniques is a bimonthly interdisciplinary publication of the Society for Experimental Mechanics focusing on the development, application and tutorial of experimental mechanics techniques.
The purpose for Experimental Techniques is to promote pedagogical, technical and practical advancements in experimental mechanics while supporting the Society''s mission and commitment to interdisciplinary application, research and development, education, and active promotion of experimental methods to:
- Increase the knowledge of physical phenomena
- Further the understanding of the behavior of materials, structures, and systems
- Provide the necessary physical observations necessary to improve and assess new analytical and computational approaches.