scholarly journals Multi-Sensor Feature Extraction and Data Fusion Using ANFIS and 2D Wavelet Transform in Structural Health Monitoring

2017 ◽  
Author(s):  
Ponciano Jorge Escamilla-Ambrosio ◽  
Xuefeng Liu ◽  
Juan Manuel Ramírez-Cortés ◽  
Abraham Rodríguez-Mota ◽  
María del Pilar Gómez-Gil
Keyword(s):  
Feature Extraction ◽  
Wavelet Transform ◽  
Structural Health Monitoring ◽  
Data Fusion ◽  
Health Monitoring ◽  
Structural Health

scholarly journals Structural health monitoring data fusion for in-situ life prognosis of composite structures

2018 ◽  
Vol 178 ◽  
pp. 40-54 ◽  
Author(s):  
Nick Eleftheroglou ◽  
Dimitrios Zarouchas ◽  
Theodoros Loutas ◽  
Rene Alderliesten ◽  
Rinze Benedictus
Keyword(s):  
Structural Health Monitoring ◽  
Data Fusion ◽  
Health Monitoring ◽  
Composite Structures ◽  
Monitoring Data ◽  
Structural Health ◽  
Life Prognosis

scholarly journals Big Data Analytics and Structural Health Monitoring: A Statistical Pattern Recognition-Based Approach

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2328 ◽  
Author(s):  
Alireza Entezami ◽  
Hassan Sarmadi ◽  
Behshid Behkamal ◽  
Stefano Mariani
Keyword(s):  
Pattern Recognition ◽  
Feature Extraction ◽  
Big Data ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Statistical Pattern Recognition ◽  
Statistical Decision ◽  
Structural Health ◽  
Statistical Pattern ◽  
Arma Modeling

Recent advances in sensor technologies and data acquisition systems opened up the era of big data in the field of structural health monitoring (SHM). Data-driven methods based on statistical pattern recognition provide outstanding opportunities to implement a long-term SHM strategy, by exploiting measured vibration data. However, their main limitation, due to big data or high-dimensional features, is linked to the complex and time-consuming procedures for feature extraction and/or statistical decision-making. To cope with this issue, in this article we propose a strategy based on autoregressive moving average (ARMA) modeling for feature extraction, and on an innovative hybrid divergence-based method for feature classification. Data relevant to a cable-stayed bridge are accounted for to assess the effectiveness and efficiency of the proposed method. The results show that the offered hybrid divergence-based method, in conjunction with ARMA modeling, succeeds in detecting damage in cases strongly characterized by big data.

Multi-rate data fusion for dynamic displacement measurement of beam-like supertall structures using acceleration and strain sensors

2019 ◽  
Vol 19 (2) ◽  
pp. 520-536 ◽  
Author(s):  
Hongping Zhu ◽  
Ke Gao ◽  
Yong Xia ◽  
Fei Gao ◽  
Shun Weng ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Data Fusion ◽  
Health Monitoring ◽  
Optimization Technique ◽  
Strain Sensors ◽  
Mode Shapes ◽  
Dynamic Displacement ◽  
Structural Health ◽  
Set Up ◽  
Longitudinal Strains

Accurate measurement of dynamic displacement is important for the structural health monitoring and safety assessment of supertall structures. However, the displacement of a supertall structure is difficult to be accurately measured using the conventional methods because they are either inaccurate or inconvenient to be set up in practice. This study provides an accurate and economical method to measure dynamic displacement of supertall structures accurately by fusing acceleration and strain data, which are generally available in the structural health monitoring system. Dynamic displacement is first derived from the measured longitudinal strains based on geometric deformation without requiring mode shapes. An optimization technique is utilized to optimize the deployment of strain sensors for achieving more accurate strain-derived displacement. The strain-derived displacement is then combined with measured acceleration via a multi-rate Kalman filtering approach. Applications to a numerical supertall structure and a laboratory cantilever beam verify that the proposed method accurately estimates displacement including both high-frequency and pseudo-static components, under different noise cases and sampling rates. A full-scale field test on the 600 m-high Canton Tower is implemented to validate the applicability of the proposed method to real supertall structures. Error analysis demonstrates that the data fusion displacement is more accurate than the global position system-measured displacement in the time and frequency domains.

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