Journal of Applied and Computational Mechanics

Manifold Learning Algorithms Applied to Structural Damage ‎Classification

Document Type : Special Issue Paper

Authors

1 Control, Modeling, Identification and Applications (CoDAlab), Department of Mathematics, Escola d’Enginyeria de Barcelona Est (EEBE), Universitat Politècnica ‎de Catalunya (UPC), Campus Diagonal-Besòs (CDB), Eduard Maristany, 16, Barcelona, 08019, Spain

2 Departamento de Ingeniería Mecánica y Mecatrónica, Universidad Nacional de Colombia, Cra 45 No. 26-85, Bogotá, 111321, Colombia‎

3 MEM (Modelling-Electronics and Monitoring Research Group), Faculty of Electronics Engineering, Universidad Santo Tomás, Bogotá 110231, Colombia

4 Departamento de Ingeniería Eléctrica y Electrónica, Universidad Nacional de Colombia, Cra 45 No. 26-85, Bogotá, 111321, Colombia‎

Abstract

A comparative study of four manifold learning algorithms was carried out to perform the dimensionality reduction process within a proposed methodology for damage classification in structural health monitoring (SHM). Isomap, locally linear embedding (LLE), stochastic proximity embedding (SPE), and laplacian eigenmaps were used as manifold learning algorithms. The methodology included several stages that comprised: data normalization, dimensionality reduction, classification through K-Nearest Neighbors (KNN) machine learning model and finally holdout cross-validation with 25% of data for training and the remaining 75% of data for testing. Results evaluated in an experimental setup showed that the best classification accuracy was 100% when the methodology uses isomap algorithm with a hyperparameter k of 170 and 8 dimensions as a feature vector at the input to the KNN classification machine.

Keywords

Main Subjects

[1] Leon, J. X., Pineda Muñoz, W. A., Anaya, M., Vitola, J., Tibaduiza, D. A., Structural Damage classification using machine learning algorithms and performance measures, 12th International Workshop on Structural Health Monitoring-IWSHM 2019, Stanford, California, USA, 2019.
[2] Vitola, J., Pozo, F., Tibaduiza, D. A., Anaya, M., A sensor data fusion system based on k-nearest neighbor pattern classification for structural health monitoring applications. Sensors, 17(2), 2017, 417.
[3] Van der Maaten, L. J. P., An introduction to dimensionality reduction using matlab. Report, 1201(07-07), 2007, 62.
[4] Sugihara, G., May, R., Ye, H., Hsieh, C. H., Deyle, E., Fogarty, M., Munch, S., Detecting causality in complex ecosystems. Science, 338(6106), 2012, 496-500.
[5] Zhu, B., Liu, J. Z., Cauley, S. F., Rosen, B. R., Rosen, M. S., Image reconstruction by domain-transform manifold learning. Nature, 555(7697), 2018, 487-492.
[6] Van Der Maaten, L. J. P., Van Den Hh, J., Dimensionality reduction: A comparative review. Tilburg, Netherlands: Tilburg Centre for Creative Computing, Tilburg University, Technical Report. 2009.
[7] Tibaduiza, D. A., Mujica, L. E., Rodellar, J., Güemes, A., Structural damage detection using principal component analysis and damage indices. Journal of Intelligent Material Systems and Structures, 27(2), 2016, 233-248.
[8] Dervilis, N., Antoniadou, I., Cross, E. J., Worden, K., A Non‐linear Manifold Strategy for SHM Approaches. Strain, 51(4), 2015, 324-331.
[9] Yildiz, K., Çamurcu, A. Y., Dogan, B., Comparison of dimension reduction techniques on high dimensional datasets. The International Arab Journal of Information Technology, 15(2), 2018, 256-262.
[10] Tibaduiza, D. A., Torres-Arredondo, M. A., Mujica, L. E., Rodellar, J., Fritzen, C. P., A study of two unsupervised data driven statistical methodologies for detecting and classifying damages in structural health monitoring. Mechanical Systems and Signal Processing, 41(1-2), 2013, 467-484.
[11] Anaya, M., Tibaduiza, D. A., Pozo, F., Detection and classification of structural changes using artificial immune systems and fuzzy clustering. International Journal of Bio-Inspired Computation, 9(1), 2017, 35-52.
[12] Tibaduiza, D., Torres-Arredondo, M. Á., Vitola, J., Anaya, M., Pozo, F., A damage classification approach for structural health monitoring using machine learning. Complexity, 2018, Article ID 5081283.
[13] Leon-Medina, J. X., Cardenas-Flechas, L. J., Tibaduiza, D. A., A data-driven methodology for the classification of different liquids in artificial taste recognition applications with a pulse voltammetric electronic tongue, International Journal of Distributed Sensor Networks, 15(10), 2019, 1-18.
[14] Zhang, L., Wang, X., Huang, G. B., Liu, T., Tan, X., Taste recognition in E-tongue using local discriminant preservation projection. IEEE transactions on Cybernetics, 49(3), 2018, 947-960.
[15] Leon-Medina, J. X., Anaya, M., Pozo, F., Tibaduiza, D. A., Application of manifold learning algorithms to improve the classification performance of an electronic nose 2020 IEEE International Instrumentation & Measurement Technology Conference (I2MTC), Dubrovnik, Croatia, 2020, 1-6.
[16] Zhang, L., Tian, F. C., A new kernel discriminant analysis framework for electronic nose recognition. Analytica Chimica Acta, 816, 2014, 8-17.
[17] Agis, D., Pozo, F., A frequency-based approach for the detection and classification of structural changes using t-SNE. Sensors, 19(23), 2019, 5097.
[18] Zhang, J., Li, S. Z., Wang, J., Manifold learning and applications in recognition. In Intelligent multimedia processing with soft computing Springer, Berlin, Heidelberg, 2005, 281-300.
[19] De Ridder, D., Duin, R. P., Locally Linear Embedding for Classification, Technical Reports, Delft University of Technology, The Netherlands, 2002.
[20] Tenenbaum, J.B., Mapping a manifold of perceptual observations. In Advances in Neural Information Processing Systems, Vol. 10, Cambridge, MA, USA. The MIT Press, 1998, 682–688.
[21] Belkin, M., Niyogi., P., Laplacian Eigenmaps and spectral techniques for embedding and clustering. In Advances in Neural Information Processing Systems, volume 14, Cambridge, MA, USA. The MIT Press, 2002, 585–591.
[22] Bull, L. A., K. Worden, R. Fuentes, G. Manson, E. J. Cross, Dervilis, N., Outlier ensembles: A robust method for damage detection and unsupervised feature extraction from high-dimensional data. Journal of Sound and Vibration, 453, 2019,126-150.
[23] Plastria, F., De Bruyne, S., Carrizosa, E., Dimensionality Reduction for Classification, 4th International Conference on Advanced Data Mining and Applications, ser. ADMA ’08, Chengdu, China, 2008, 411–418.
[24] Anderson, W.N., Morley, T.D., Eigenvalues of the Laplacian of a graph. Linear and Multilinear Algebra, 18(2), 1985, 141–145.
[25] Cox, T. and M. Cox., Multidimensional scaling. Chapman and Hall, London, UK, 1994.
[26] Ma, Y., and Y. Fu., Manifold learning theory and applications. CRC press, Boca Raton, FL, USA, 2011.
[27] Roweis, S.T., Saul, L.K., Nonlinear dimensionality reduction by Locally Linear Embedding. Science, 290(5500), 2000, 2323–2326.
[28] Agrafiotis, D. K., Stochastic proximity embedding. Journal of Computational Chemistry, 24(10), 2003, 1215-1221.
[29] Pozo, F., Vidal, Y., Salgado, Ó. Wind turbine condition monitoring strategy through multiway PCA and multivariate inference. Energies, 11(4), 2018, 749.
[30] Ballabio, D., F. Grisoni, Todeschini, R., Multivariate comparison of classification performance measures. Chemometrics and Intelligent Laboratory Systems, 174, 2018, 33-44.
Journal of Applied and Computational Mechanics
Volume 7, Special Issue
June 2021
Pages 1158-1166