research-article

On Structural Health Monitoring Using Tensor Analysis and Support Vector Machine with Artificial Negative Data

Authors:

Nguyen Lu Dang Khoa,

Mehrisadat Makki Alamdari,

Peter RuncieAuthors Info & Claims

CIKM '16: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management

Pages 1813 - 1822

https://doi.org/10.1145/2983323.2983359

Published: 24 October 2016 Publication History

Abstract

Structural health monitoring is a condition-based technology to monitor infrastructure using sensing systems. Since we usually only have data associated with the healthy state of a structure, one-class approaches are more practical. However, tuning the parameters for one-class techniques (like one-class Support Vector Machines) still remains a relatively open and difficult problem. Moreover, in structural health monitoring, data are usually multi-way, highly redundant and correlated, which a matrix-based two-way approach cannot capture all these relationships and correlations together. Tensor analysis allows us to analyse the multi-way vibration data at the same time. In our approach, we propose the use of tensor learning and support vector machines with artificial negative data generated by density estimation techniques for damage detection, localization and estimation in a one-class manner. The artificial negative data can help tuning SVM parameters and calibrating probabilistic outputs, which is not possible to do with one-class SVM. The proposed method shows promising results using data from laboratory-based structures and also with data collected from the Sydney Harbour Bridge, one of the most iconic structures in Australia. The method works better than the one-class approach and the approach without using tensor analysis.

References

[1]

E. Acar and B. Yener. Unsupervised multiway data analysis: A literature survey. IEEE Transactions on Knowledge and Data Engineering, 21(1):6--20, 2009.

Digital Library

[2]

M. M. Alamdari. Vibration-based Structural Health Monitoring. PhD thesis, 2015.

[3]

C. A. Andersson and R. Bro. The n-way toolbox forMATLAB\. Chemometrics and Intelligent Laboratory Systems, 52(1):1--4, 2000.

[4]

B. W. Bader, T. G. Kolda, et al. Matlab tensor toolbox version 2.5. Available online, January 2012.

[5]

C. M. Bishop. Pattern Recognition and Machine Learning. Springer, Singapore, 2013.

[6]

M. M. Breunig, H.-P. Kriegel, R. T. Ng, and J. Sander. Lof: Identifying density-based local outliers. In International Conference on Management of Data, Dallas, Texas, 2000. ACM SIGMOD.

Digital Library

[7]

R. Brincker, L. Zhang, and P. Andersen. Modal identification from ambient responses using frequency domain decomposition. In IMAC 18 : Proceedings of the International Modal Analysis Conference (IMAC), San Antonio, Texas, USA, February 7-10, 2000, pages 625--630, 2000.

[8]

R. Bro and H. A. L. Kiers. A new efficient method for determining the number of components in parafac models. J. of Chemometrics, 17(5):274--286, 2003.

[9]

T. H. Chan, Y.-Q. Ni, and J. M. Ko. Neural network novelty filtering for anomaly detection. In F. Cheng, editor, 2nd International Workshop on Structural Health Monitoring, pages 133--137, Standford, USA, 1999. Technomic Pub. Co.

[10]

C.-C. Chang and C.-J. Lin. LIBSVM: A library for support vector machines. ACM Trans. on Intelligent Systems and Technology, 2:27:1--27:27, 2011.

Digital Library

[11]

P. Cheema, N. F. Giannelis, and G. A. Vio. Experimental validation of polynomial chaos theory on an aircraft t-tail. In 18th AIAA Non-Deterministic Approaches Conference, 2016.

[12]

C. R. Farrar and K. Worden. An introduction to structural health monitoring. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 365(1851):303--315, 2007.

[13]

J. Gao and P.-N. Tan. Converting output scores from outlier detection algorithms into probability estimates. In Data Mining, 2006. ICDM'06. Sixth International Conference on, pages 212--221. IEEE, 2006.

Digital Library

[14]

S. Khazai, S. Homayouni, A. Safari, and B. Mojaradi. Anomaly detection in hyperspectral images based on an adaptive support vector method. IEEE Geoscience and Remote Sensing Letters, 8(4):646--650, 2011.

[15]

N. L. D. Khoa, B. Zhang, Y. Wang, W. Liu, F. Chen, S. Mustapha, and P. Runcie. PAKDD 2015, Vietnam, May 19--22, 2015, Proceedings, Part I, chapter On Damage Identification in Civil Structures Using Tensor Analysis, pages 459--471. Springer International Publishing, Cham, 2015.

[16]

T. G. Kolda and B. W. Bader. Tensor decompositions and applications. SIAM Review, 51(3):455--500, September 2009.

Digital Library

[17]

T. G. Kolda and J. Sun. Scalable tensor decompositions for multi-aspect data mining. In ICDM 2008: Proceedings of the 8th IEEE International Conference on Data Mining, pages 363--372, December 2008.

Digital Library

[18]

LANL. Los alamos national laboratory website, 2013. (last visited: 01/06/2013).

[19]

L. Manevitz and M. Yousef. One-class svms for document classification. Machine Learning Research, 2:139--154, 2002.

Digital Library

[20]

K. Matej, A. Leonardis, and S. Danijel. Multivariate online kernel density estimation with gaussian kernels. Pattern Recognition, 44:2630--2642, 2011.

Digital Library

[21]

I. T. Nabney. NETLAB: Algorithms for Pattern Recognition. Springer, 2002.

Digital Library

[22]

J. C. Platt. Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In Advances In Large Margin Classifiers, pages 61--74. MIT Press, 1999.

[23]

M. A. Prada, J. Toivola, J. Kullaa, and J. Hollmén. Three-way analysis of structural health monitoring data. Neurocomputing, 80(0):119--128, 2012. Special Issue on Machine Learning for Signal Processing 2010.

Digital Library

[24]

P. J. Rousseeuw. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of computational and applied mathematics, 20:53--65, 1987.

Digital Library

[25]

P. Runcie, S. Mustapha, and T. Rakotoarivelo. Advances in structural health monitoring system architecture. In Proceedings of the the fourth International Symposium on Life-Cycle Civil Engineering, IALCCE '14, pages 1064--1071, 2014.

[26]

A. Rytter. Vibration-based inspection of civil engineering structures. PhD thesis, University of Aalborg, Denmark, 1993.

[27]

B. Schölkopf, J. C. Platt, J. C. Shawe-Taylor, A. J. Smola, and R. C. Williamson. Estimating the support of a high-dimensional distribution. Neural Computation, 13(7):1443--1471, July 2001.

Digital Library

[28]

V. A. Sotiris, P. W. Tse, and M. G. Pecht. Anomaly detection through bayesian support vector machine. IEEE Transactions on Reliability, 59:277--286, 2010.

[29]

D. M. Tax and R. P. Duin. Support vector data description. Machine Learning, 54:45--66, 2004.

Digital Library

[30]

K. Worden and G. Manson. The application of machine learning to structural health monitoring. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 365(1851):515--537, 2007.

[31]

K. Worden, G. Manson, and N. Fieller. Damage detection using outlier analysis. Journal of Sound and Vibration, 229(3):647--667, 2000.

[32]

G. D. Wyss and K. H. Jorgensen. A user's guide to lhs: Sandia's latin hypercube sampling software. Technical report, Sandia National Laboratories, 1998.

Cited By

Cheema PAlamdari MChang KKim CSugiyama M(2022)A drive-by bridge inspection framework using non-parametric clusters over projected data manifoldsMechanical Systems and Signal Processing10.1016/j.ymssp.2022.109401180(109401)Online publication date: Nov-2022
https://doi.org/10.1016/j.ymssp.2022.109401
Peng XLi HYuan FRazul SChen ZLin Z(2022)An extreme learning machine for unsupervised online anomaly detection in multivariate time seriesNeurocomputing10.1016/j.neucom.2022.06.042501:C(596-608)Online publication date: 28-Aug-2022
https://dl.acm.org/doi/10.1016/j.neucom.2022.06.042
Chalapathy RKhoa N(2021)Comparison of unsupervised shallow and deep models for structural health monitoringProceedings of the Institution of Civil Engineers - Bridge Engineering10.1680/jbren.21.00016(1-11)Online publication date: 24-Nov-2021
https://doi.org/10.1680/jbren.21.00016
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On Structural Health Monitoring Using Tensor Analysis and Support Vector Machine with Artificial Negative Data

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cover image ACM Conferences

CIKM '16: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management

October 2016

2566 pages

ISBN:9781450340731

DOI:10.1145/2983323

General Chairs:
Snehasis Mukhopadhyay
Indiana University Purdue University Indianapolis, USA
,
ChengXiang Zhai
University of Illinois at Urbana-Champaign, USA
,
Program Chairs:
Elisa Bertino
Purdue University
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Fabio Crestani
University of Lugano
,
Javed Mostafa
University of North Carolina
,
Jie Tang
Tsinghua University
,
Luo Si
Alibaba Group Inc & Purdue University
,
Xiaofang Zhou
University of Queensland
,
Yi Chang
Yahoo Research
,
Yunyao Li
IBM Research - Almaden
,
Parikshit Sondhi
WalmartLabs

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Published: 24 October 2016

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Cited By

Cheema PAlamdari MChang KKim CSugiyama M(2022)A drive-by bridge inspection framework using non-parametric clusters over projected data manifoldsMechanical Systems and Signal Processing10.1016/j.ymssp.2022.109401180(109401)Online publication date: Nov-2022
https://doi.org/10.1016/j.ymssp.2022.109401
Peng XLi HYuan FRazul SChen ZLin Z(2022)An extreme learning machine for unsupervised online anomaly detection in multivariate time seriesNeurocomputing10.1016/j.neucom.2022.06.042501:C(596-608)Online publication date: 28-Aug-2022
https://dl.acm.org/doi/10.1016/j.neucom.2022.06.042
Chalapathy RKhoa N(2021)Comparison of unsupervised shallow and deep models for structural health monitoringProceedings of the Institution of Civil Engineers - Bridge Engineering10.1680/jbren.21.00016(1-11)Online publication date: 24-Nov-2021
https://doi.org/10.1680/jbren.21.00016
Anaissi ASuleiman BZandavi S(2021)Online Tensor-Based Learning Model for Structural Damage DetectionACM Transactions on Knowledge Discovery from Data10.1145/345121715:6(1-18)Online publication date: 19-May-2021
https://dl.acm.org/doi/10.1145/3451217
Ruff LKauffmann JVandermeulen RMontavon GSamek WKloft MDietterich TMuller K(2021)A Unifying Review of Deep and Shallow Anomaly DetectionProceedings of the IEEE10.1109/JPROC.2021.3052449109:5(756-795)Online publication date: May-2021
https://doi.org/10.1109/JPROC.2021.3052449
Anaissi ASuleiman BZandavi S(2020)Online Tensor Decomposition with optimized Stochastic Gradient Descent: an Application in Structural Damage Identification2020 IEEE Symposium Series on Computational Intelligence (SSCI)10.1109/SSCI47803.2020.9308310(1257-1264)Online publication date: 1-Dec-2020
https://doi.org/10.1109/SSCI47803.2020.9308310
Cheema PNguyen KKidd MVio G(2019)A Tensor-based Structural Health Monitoring Approach for Aeroservoelastic SystemsAIAA Scitech 2019 Forum10.2514/6.2019-1964Online publication date: 6-Jan-2019
https://doi.org/10.2514/6.2019-1964
Menon AWilliamson R(2018)A loss framework for calibrated anomaly detectionProceedings of the 32nd International Conference on Neural Information Processing Systems10.5555/3326943.3327080(1494-1504)Online publication date: 3-Dec-2018
https://dl.acm.org/doi/10.5555/3326943.3327080
Rébillat MMechbal N(2018)Damage localization in composite plates using canonical polyadic decomposition of Lamb wave difference signals tensorIFAC-PapersOnLine10.1016/j.ifacol.2018.09.64751:24(668-673)Online publication date: 2018
https://doi.org/10.1016/j.ifacol.2018.09.647
Khoa NAnaissi AWang YLim EWinslett MSanderson MFu ASun JCulpepper SLo EHo JDonato DAgrawal RZheng YCastillo CSun ATseng VLi C(2017)Smart Infrastructure Maintenance Using Incremental Tensor AnalysisProceedings of the 2017 ACM on Conference on Information and Knowledge Management10.1145/3132847.3132851(959-967)Online publication date: 6-Nov-2017
https://dl.acm.org/doi/10.1145/3132847.3132851

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