scholarly journals Structural health monitoring algorithm comparisons using standard data sets

2009 ◽  
Author(s):  
Eloi Figueiredo ◽  
Gyuhae Park ◽  
Joaquim Figueiras ◽  
Charles Farrar ◽  
Keith Worden
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Data Sets ◽  
Structural Health ◽  
Standard Data ◽  
Monitoring Algorithm

scholarly journals Structural health monitoring algorithm application to a powerboat model impacting on water surface

2019 ◽  
Vol 24 ◽  
pp. 926-938 ◽  
Author(s):  
Pierluigi Fanelli ◽  
Simone Trupiano ◽  
Valerio Gioachino Belardi ◽  
Francesco Vivio ◽  
Elio Jannelli
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Water Surface ◽  
Structural Health ◽  
Monitoring Algorithm

Efficient storage and processing of large guided wave data sets with random projections

2020 ◽  
pp. 147592172096019
Author(s):  
Sungwon Kim ◽  
Spencer Shiveley ◽  
Alexander CS Douglass ◽  
Yisong Zhang ◽  
Rajeev Sahay ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Damage Detection ◽  
Health Monitoring ◽  
Random Projection ◽  
Guided Wave ◽  
Random Projections ◽  
Data Sets ◽  
Structural Health ◽  
Efficient Storage ◽  
Value Decomposition

Over the last several decades, structural health monitoring systems have grown into increasingly diverse applications. Structural health monitoring excels with large data sets that can capture the typical variability, novel events, and undesired degradation over time. As a result, the efficient storage and processing of these large, guided wave data sets have become a key feature for successful application of structural health monitoring. This article describes a series of investigations into the use of random projection theory to significantly reduce storage burdens and improve computational complexity while not significantly affecting common damage detection strategies. Random projections are used as a lossy compression scheme that approximately retains metrics of distance or similarity between data records. Random projection compression is evaluated using a large 1,440,000 measurement data set, which was collected over 5 months in an unprotected outdoor environment. Accurate damage detection, after the compression process, is achieved through correlation analysis and singular value decomposition. The results indicate consistent detection performance with over 95% of storage compression and more than a 477 times speed improvement in computational cost for singular value decomposition–based damage detection.

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