Dimensionality Reduction Using Linear and Nonlinear Transformation

Kerschen, Gaëtan; Golinval, Jean‐Claude

doi:10.1002/9780470061626.shm050

Cited by 1 publication

(2 citation statements)

References 48 publications

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“…Hierarchical nonlinear PCA, or simple h-NLPCA, is a variation of the well-known PCA [28]. Its main advantage is the ability to deal with nonlinear problems in a more effective way than the PCA [43].…”

Section: Hierarchical Nonlinear Principal Component Analysis (Pca)mentioning

confidence: 99%

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Damage detection methodology under variable load conditions based on strain field pattern recognition using FBGs, nonlinear principal component analysis, and clustering techniques

Sierra-Pérez

Torres-Arredondo

2017

Smart Mater. Struct.

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Structural health monitoring consists of using sensors integrated within structures together with algorithms to perform load monitoring, damage detection, damage location, damage size and severity, and prognosis. One possibility is to use strain sensors to infer structural integrity by comparing patterns in the strain field between the pristine and damaged conditions. In previous works, the authors have demonstrated that it is possible to detect small defects based on strain field pattern recognition by using robust machine learning techniques. They have focused on methodologies based on principal component analysis (PCA) and on the development of several unfolding and standardization techniques, which allow dealing with multiple load conditions. However, before a real implementation of this approach in engineering structures, changes in the strain field due to conditions different from damage occurrence need to be isolated. Since load conditions may vary in most engineering structures and promote significant changes in the strain field, it is necessary to implement novel techniques for uncoupling such changes from those produced by damage occurrence. A damage detection methodology based on optimal baseline selection (OBS) by means of clustering techniques is presented. The methodology includes the use of hierarchical nonlinear PCA as a nonlinear modeling technique in conjunction with Q and nonlinear-T 2 damage indices. The methodology is experimentally validated using strain measurements obtained by 32 fiber Bragg grating sensors bonded to an aluminum beam under dynamic bending loads and simultaneously submitted to variations in its pitch angle. The results demonstrated the capability of the methodology for clustering data according to 13 different load conditions (pitch angles), performing the OBS and detecting six different damages induced in a cumulative way. The proposed methodology showed a true positive rate of 100% and a false positive rate of 1.28% for a 99% of confidence.

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Section: Hierarchical Nonlinear Principal Component Analysis (Pca)mentioning

confidence: 99%

“…In such cases, when a PCA analysis is performed, such nonlinearities can affect the performance of the analysis. Therefore, an h-NLPCA analysis should be used instead of the linear PCA in order to obtain a more efficient description of the data by means of nonlinear transformations [43].…”

Section: Hierarchical Nonlinear Principal Component Analysis (Pca)mentioning

confidence: 99%

Damage detection methodology under variable load conditions based on strain field pattern recognition using FBGs, nonlinear principal component analysis, and clustering techniques

Sierra-Pérez

Torres-Arredondo

2017

Smart Mater. Struct.

View full text Add to dashboard Cite

show abstract

Dimensionality Reduction Using Linear and Nonlinear Transformation

Cited by 1 publication

References 48 publications

Damage detection methodology under variable load conditions based on strain field pattern recognition using FBGs, nonlinear principal component analysis, and clustering techniques

Damage detection methodology under variable load conditions based on strain field pattern recognition using FBGs, nonlinear principal component analysis, and clustering techniques

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