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Probabilistic-based damage identification based on error functions with an autofocusing feature

  • Gorgin, Rahim (State Key Laboratory of Structural Analysis for Industry Equipments, School of Aeronautics and Astronautics, Dalian University of Technology) ;
  • Ma, Yunlong (Beijing Aerospace System Engineering Institute) ;
  • Wu, Zhanjun (State Key Laboratory of Structural Analysis for Industry Equipments, School of Aeronautics and Astronautics, Dalian University of Technology) ;
  • Gao, Dongyue (State Key Laboratory of Structural Analysis for Industry Equipments, School of Aeronautics and Astronautics, Dalian University of Technology) ;
  • Wang, Yishou (State Key Laboratory of Structural Analysis for Industry Equipments, School of Aeronautics and Astronautics, Dalian University of Technology)
  • Received : 2014.05.09
  • Accepted : 2014.11.18
  • Published : 2015.04.25
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Abstract

This study presents probabilistic-based damage identification technique for highlighting damage in metallic structures. This technique utilizes distributed piezoelectric transducers to generate and monitor the ultrasonic Lamb wave with narrowband frequency. Diagnostic signals were used to define the scatter signals of different paths. The energy of scatter signals till different times were calculated by taking root mean square of the scatter signals. For each pair of parallel paths an error function based on the energy of scatter signals is introduced. The resultant error function then is used to estimate the probability of the presence of damage in the monitoring area. The presented method with an autofocusing feature is applied to aluminum plates for method verification. The results identified using both simulation and experimental Lamb wave signals at different central frequencies agreed well with the actual situations, demonstrating the potential of the presented algorithm for identification of damage in metallic structures. An obvious merit of the presented technique is that in addition to damages located inside the region between transducers; those who are outside this region can also be monitored without any interpretation of signals. This novelty qualifies this method for online structural health monitoring.

Keywords

References

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