Title
Structural Health Monitoring based on Optical Scanning Systems and SVM
Abstract
This paper presents a new approach for damage detection in Structural Health Monitoring (SHM) Systems, which is based on Optical Scanning and Support Vector Machine (SVM) models. Optical Scanning Systems provide position measurements for SHM task by a novel method based on automatic geodetic measurements. Precise measurement of plane spatial angles are performed in the optical energy signal centre by the optical signal function geometric centroid calculation, however these scanners usually have non-linear variations in their measurement, and normally these variations depend on the position of the light emitter on the structure under monitoring in relation to the scanner. In this paper, SVM Regression is proposed as a machine learning technique to predict measurement errors and to adjust this non-linear variation for measurement accuracy enhancement.
Year
DOI
Venue
2014
10.1109/ISIE.2014.6864916
ISIE
Keywords
Field
DocType
condition monitoring,learning (artificial intelligence),measurement errors,position measurement,regression analysis,structural engineering computing,support vector machines,shm,svm,svm regression,automatic geodetic measurements,damage detection,light emitter,machine learning technique,measurement accuracy enhancement,measurement error prediction,nonlinear variation,optical scanning systems,optical signal function geometric centroid,structural health monitoring,support vector machine model,energy signal centre,error correction,geometric centroid,measurements,optical scanning,support vector machine,kernel,learning artificial intelligence,adaptive optics,measurement uncertainty,nonlinear optics
Computer vision,Geodetic datum,Structural health monitoring,Common emitter,Support vector machine,Control engineering,Scanner,Artificial intelligence,Accuracy and precision,Engineering,Observational error,Centroid
Conference
ISSN
Citations 
PageRank 
2163-5137
1
0.41
References 
Authors
3
8