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A fast and efficient feature extraction methodology for structural damage localization based on raw acceleration measurements
Author(s) -
Alves Victor,
Cury Alexandre
Publication year - 2021
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.2748
Subject(s) - structural health monitoring , outlier , computer science , feature extraction , acceleration , feature (linguistics) , frame (networking) , pattern recognition (psychology) , process (computing) , artificial intelligence , data mining , engineering , structural engineering , linguistics , philosophy , physics , classical mechanics , telecommunications , operating system
Summary Many damage detection strategies have been developed within the field of structural health monitoring, showing promising results in real‐world applications. Most of them rely on the use of modal parameters to locate damage in structures. However, they are highly dependent on the process of extracting such characteristics, which contains uncertainties and needs to be adapted specifically for each structure. Recently, a special focus is being given to techniques based only on the use of raw structural acceleration measurements due to their relatively lower computational complexity manipulation. Thus, this work proposes an efficient feature extraction methodology for structural damage localization based on raw vibration signals. Furthermore, its ability to indicate damage quantification is also investigated. The proposed approach consists in extracting sensitive features from time, frequency, and quefrency domains. Then, percentile intervals are defined for each feature regarding the structures' healthy state. Finally, a damage index is estimated based on the number of outlier features of the structures' damaged state. In this sense, the sensor with the highest number of outlier features indicates the damage location. To assess this methodology, four different applications are studied: a numerical two‐dimensional frame, a simply supported beam tested in laboratory, a three‐dimensional frame also tested in laboratory, and the Tianjin Yonghe Bridge in China. Results show that the proposed approach is not only able to correctly indicate damage locations but also to give insights about their magnitudes.