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EEMD-MUSIC-based analysis for natural frequencies identification of structures using artificial and natural excitations.

David Camarena-MartinezJuan P Amezquita-SanchezMartin Valtierra-RodriguezRene J Romero-TroncosoRoque A Osornio-RiosArturo Garcia-Perez
Published in: TheScientificWorldJournal (2014)
This paper presents a new EEMD-MUSIC- (ensemble empirical mode decomposition-multiple signal classification-) based methodology to identify modal frequencies in structures ranging from free and ambient vibration signals produced by artificial and natural excitations and also considering several factors as nonstationary effects, close modal frequencies, and noisy environments, which are common situations where several techniques reported in literature fail. The EEMD and MUSIC methods are used to decompose the vibration signal into a set of IMFs (intrinsic mode functions) and to identify the natural frequencies of a structure, respectively. The effectiveness of the proposed methodology has been validated and tested with synthetic signals and under real operating conditions. The experiments are focused on extracting the natural frequencies of a truss-type scaled structure and of a bridge used for both highway traffic and pedestrians. Results show the proposed methodology as a suitable solution for natural frequencies identification of structures from free and ambient vibration signals.
Keyphrases
  • air pollution
  • systematic review
  • high frequency
  • high resolution
  • particulate matter
  • machine learning
  • deep learning