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Impact event identification in thin plates through real strain measurements
Author(s) -
Yang Mijia,
Ahmari Saeed,
Selekwa Majura
Publication year - 2017
Publication title -
structural control and health monitoring
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.587
H-Index - 62
eISSN - 1545-2263
pISSN - 1545-2255
DOI - 10.1002/stc.1933
Subject(s) - inverse , inverse filter , identification scheme , signal (programming language) , identification (biology) , structural health monitoring , algorithm , computer science , inverse problem , energy (signal processing) , filter (signal processing) , event (particle physics) , mathematics , data mining , statistics , engineering , structural engineering , computer vision , mathematical analysis , geometry , physics , botany , quantum mechanics , biology , programming language , measure (data warehouse)
Summary Impact event identification is a primary concern in many structural health monitoring applications. Model‐based inverse analysis is a common approach for system identification as long as the physical model can accurately capture the behavior of structure. A layered analysis including estimation of impact location in the first layer and refinement and reconstruction of impact load time history through inverse analysis in the second layer was proposed and verified well with the numerical results previously. Due to unavoidable noises, implementation of the theory with a modified inverse scheme on a simply supported plate structure is conducted in this study. The real signal data is first prefiltered using a low pass filter. Different alternatives are then proposed for the inverse scheme, and a new parameter, normalized signal energy, is adopted for layer 1 due to its convenience and accuracy compared with the traditional one, that is, the time of flight method. Signal power and filtered signal data are used for layer 2 of the inverse procedure. The results indicate that the modified inverse scheme is capable of detecting impact location and reconstructing impact load time history with a satisfactory precision.