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Adaptive One-Class Support Vector Machine for Damage Detection in Structural Health Monitoring

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10234)

Abstract

Machine learning algorithms have been employed extensively in the area of structural health monitoring to compare new measurements with baselines to detect any structural change. One-class support vector machine (OCSVM) with Gaussian kernel function is a promising machine learning method which can learn only from one class data and then classify any new query samples. However, generalization performance of OCSVM is profoundly influenced by its Gaussian model parameter \(\sigma \). This paper proposes a new algorithm named Appropriate Distance to the Enclosing Surface (ADES) for tuning the Gaussian model parameter. The semantic idea of this algorithm is based on inspecting the spatial locations of the edge and interior samples, and their distances to the enclosing surface of OCSVM. The algorithm selects the optimal value of \(\sigma \) which generates a hyperplane that is maximally distant from the interior samples but close to the edge samples. The sets of interior and edge samples are identified using a hard margin linear support vector machine. The algorithm was successfully validated using sensing data collected from the Sydney Harbour Bridge, in addition to five public datasets. The designed ADES algorithm is an appropriate choice to identify the optimal value of \(\sigma \) for OCSVM especially in high dimensional datasets.

Keywords

Machine learning Structural health monitoring One-class support vector machine Gaussian parameter selection Anomaly detection 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Data61|CSIROEveleighAustralia
  2. 2.Department of Mechanical EngineeringAmerican University of Beirut BeirutLebanon
  3. 3.Faculty of Engineering and ITUniversity of Technology SydneySydneyAustralia

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