Data fusion approaches for structural health monitoring and system identification: Past, present, and future

2018 ◽  
Vol 19 (2) ◽  
pp. 552-586 ◽  
Author(s):  
Rih-Teng Wu ◽  
Mohammad Reza Jahanshahi
Keyword(s):  
Structural Health Monitoring ◽  
Data Fusion ◽  
Health Monitoring ◽  
Health Assessment ◽  
Heterogeneous Data ◽  
Future Research ◽  
Theoretical Concepts ◽  
Structural Health ◽  
Multiple Sensors ◽  
Robust Decisions

During the past decades, significant efforts have been dedicated to develop reliable methods in structural health monitoring. The health assessment for the target structure of interest is achieved through the interpretation of collected data. At the beginning of the 21st century, the rapid advances in sensor technologies and data acquisition platforms have led to the new era of Big Data, where a huge amount of heterogeneous data are collected by a variety of sensors. The increasing accessibility and diversity of the data resources provide new opportunities for structural health monitoring, while the aggregation of information obtained from multiple sensors to make robust decisions remains a challenging problem. This article presents a comprehensive review of the recent data fusion applications in structural health monitoring. State-of-the-art theoretical concepts and applications of data fusion in structural health monitoring are presented. Challenges for data fusion in structural health monitoring are discussed, and a roadmap is provided for future research in this area.

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 The role of dynamic response parameters in damage prediction

2019 ◽  
Vol 233 (13) ◽  
pp. 4620-4636 ◽  
Author(s):  
Behzad Ahmed Zai ◽  
MA Khan ◽  
Kamran A Khan ◽  
Asif Mansoor ◽  
Aqueel Shah ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Dynamic Response ◽  
Health Monitoring ◽  
Structural Damage ◽  
Mode Shapes ◽  
Future Research ◽  
Modal Parameter ◽  
Structural Health ◽  
Structural Applications

This article presents a literature review of published methods for damage identification and prediction in mechanical structures. It discusses ways which can identify and predict structural damage from dynamic response parameters such as natural frequencies, mode shapes, and vibration amplitudes. There are many structural applications in which dynamic loads are coupled with thermal loads. Hence, a review on those methods, which have discussed structural damage under coupled loads, is also presented. Structural health monitoring with other techniques such as elastic wave propagation, wavelet transform, modal parameter, and artificial intelligence are also discussed. The published research is critically analyzed and the role of dynamic response parameters in structural health monitoring is discussed. The conclusion highlights the research gaps and future research direction.

scholarly journals Big Data Analytics in Online Structural Health Monitoring

2020 ◽  
Vol 7 (4) ◽  
Author(s):  
Guowei Cai ◽  
Sankaran Mahadevan
Keyword(s):  
Big Data ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Data Analytics ◽  
Structural Damage ◽  
Big Data Analytics ◽  
High Volume ◽  
Heterogeneous Data ◽  
Sensor Technology ◽  
Structural Health

This manuscript explores the application of big data analytics in online structural health monitoring. As smart sensor technology is making progress and low cost online monitoring is increasingly possible, large quantities of highly heterogeneous data can be acquired during the monitoring, thus exceeding the capacity of traditional data analytics techniques. This paper investigates big data techniques to handle the highvolume data obtained in structural health monitoring. In particular, we investigate the analysis of infrared thermal images for structural damage diagnosis. We explore the MapReduce technique to parallelize the data analytics and efficiently handle the high volume, high velocity and high variety of information. In our study, MapReduce is implemented with the Spark platform, and image processing functions such as uniform filter and Sobel filter are wrapped in the mappers. The methodology is illustrated with concrete slabs, using actual experimental data with induced damage

scholarly journals Geographical Area Network—Structural Health Monitoring Utility Computing Model

2019 ◽  
Vol 8 (3) ◽  
pp. 154
Author(s):  
Hasan Tariq ◽  
Anas Tahir ◽  
Farid Touati ◽  
Mohammed Abdulla E. Al-Hitmi ◽  
Damiano Crescini ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Geographical Area ◽  
Health Assessment ◽  
Prediction Algorithm ◽  
Area Network ◽  
Utility Computing ◽  
Structural Health ◽  
Computing Model ◽  
Effective Manner

In view of intensified disasters and fatalities caused by natural phenomena and geographical expansion, there is a pressing need for a more effective environment logging for a better management and urban planning. This paper proposes a novel utility computing model (UCM) for structural health monitoring (SHM) that would enable dynamic planning of monitoring systems in an efficient and cost-effective manner in form of a SHM geo-informatics system. The proposed UCM consists of networked SHM systems that send geometrical SHM variables to SHM-UCM gateways. Every gateway is routing the data to SHM-UCM servers running a geo-spatial patch health assessment and prediction algorithm. The inputs of the prediction algorithm are geometrical variables, environmental variables, and payloads. The proposed SHM-UCM is unique in terms of its capability to manage heterogeneous SHM resources. This has been tested in a case study on Qatar University (QU) in Doha Qatar, where it looked at where SHM nodes are distributed along with occupancy density in each building. This information was taken from QU routers and zone calculation models and were then compared to ideal SHM system data. Results show the effectiveness of the proposed model in logging and dynamically planning SHM.

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.

Structural Health Monitoring with Data Fusion Method

2010 ◽  
Author(s):  
Xiukuan Zhao ◽  
Haichang Gu ◽  
Gangbing Song ◽  
Y. L. Mo ◽  
Jinwu Xu
Keyword(s):  
Structural Health Monitoring ◽  
Data Fusion ◽  
Health Monitoring ◽  
Fusion Method ◽  
Structural Health
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