Scientific Research and Essays

  • Abbreviation: Sci. Res. Essays
  • Language: English
  • ISSN: 1992-2248
  • DOI: 10.5897/SRE
  • Start Year: 2006
  • Published Articles: 2768

Full Length Research Paper

Structural damping identification using a recursive Kalman filter

Bruna Tavares Vieira da Silva
  • Bruna Tavares Vieira da Silva
  • Engineering College of Resende (FER ? AEDB) ? Resende, Brazil.
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Alvaro Manoel de Souza Soares
  • Alvaro Manoel de Souza Soares
  • Department of Mechanical Engineering, University of Taubate, Taubate, Brazil.
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Joao Bosco Goncalves
  • Joao Bosco Goncalves
  • Department of Electrical Engineering, University of Taubate, Taubate, Brazil.
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  •  Received: 07 May 2015
  •  Accepted: 10 July 2015
  •  Published: 30 July 2015

Abstract

Much research has been done in developing techniques for identifying the structural damping of physical systems. Such techniques are always accompanied by the development of an analytical model of the ideal system and its comparison with experimental data obtained in laboratory. Also, flexible systems are difficult to be modeled, but the authors can use an approximation, supposing that a flexible system, composed by a cantilever beam, can be similar to a massa-spring-damper system. In this work, are shown a recursive technique for identifying the structural damping of a physical system and its applications. The authors identified the structural damping of a system consisting of a flexible beam clamped where the authors use a mass-spring-damper model to represent it. The excitation of the system was carried out using an impact hammer in order to use such data at the input of the analytical model obtained for the system. For the flexible system, the authors implemented the methodology of recursive Kalman’s filter, in order to identify the flexibility and damping coefficients. The results show that the technique has been successfully applied once the error obtained by comparing the experimental and analytical data is quite small.
 
Key words: Identification, modeling, structural damp, Kalman filter.