Experimental tests of substructure approaches for health monitoring system

Lijun Xie, Longxi Luo, Akira Mita, Maria Q. Feng

研究成果: Conference contribution

抄録

Two substructure approaches combined with different statistical techniques are presented in the paper to reduce the challenges in the health monitoring system, such as the computation complexity, the number of unknown parameters and the number of sensors. In the approaches, a complete structure is divided into smaller substructures which are modelled as a series of single-degree-of-freedom (SDOF) systems by manipulating the equations of motion of the substructures. Newmark's method is utilized to construct the discrete systems only containing accelerations from the continuous systems. The structural parameters are then separately identified in each system using statistical techniques. A new substructure approach incorporated with the constrained least square algorithm is proposed by the authors. This new approach along with the previous substructure approach based on the ARMAX models are both evaluated and compared in two laboratory experiments including system identification of a three-story structure and damage detection of a two-story structure. All the possible ways to segment the shear structure into substructures are also investigated in the study, where the substructures with lower model errors are selected. The proposed substructure approaches are able to estimate structural parameters every two seconds, and the structural performance is evaluated through the rates of changes of structural parameters. A real-time structural health monitoring (SHM) system can be realized based on the proposed substructure approaches with processing several accelerations each time.

本文言語English
ホスト出版物のタイトルStructural Health Monitoring 2017
ホスト出版物のサブタイトルReal-Time Material State Awareness and Data-Driven Safety Assurance - Proceedings of the 11th International Workshop on Structural Health Monitoring, IWSHM 2017
編集者Fu-Kuo Chang, Fotis Kopsaftopoulos
出版社DEStech Publications
ページ1099-1106
ページ数8
ISBN(電子版)9781605953304
DOI
出版ステータスPublished - 2017
イベント11th International Workshop on Structural Health Monitoring 2017: Real-Time Material State Awareness and Data-Driven Safety Assurance, IWSHM 2017 - Stanford, United States
継続期間: 2017 9 122017 9 14

出版物シリーズ

名前Structural Health Monitoring 2017: Real-Time Material State Awareness and Data-Driven Safety Assurance - Proceedings of the 11th International Workshop on Structural Health Monitoring, IWSHM 2017
1

Other

Other11th International Workshop on Structural Health Monitoring 2017: Real-Time Material State Awareness and Data-Driven Safety Assurance, IWSHM 2017
国/地域United States
CityStanford
Period17/9/1217/9/14

ASJC Scopus subject areas

  • 健康情報管理
  • コンピュータ サイエンスの応用

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