Abstract
Despite the advances in Structural Health Monitoring (SHM) which provides actionable information on the current and future states of infrastructures, it is still challenging to fuse data properly from heterogeneous sources for robust damage identification. To address this challenge, the sensor data fusion in SHM is formulated as an incremental tensor learning problem in this paper. A novel method for online data fusion from heterogeneous sources based on incrementally-coupled tensor learning has been proposed. When new data are available, decomposed component matrices from multiple tensors are updated collectively and incrementally. A case study in SHM has been developed for sensor data fusion and online damage identification, where the SHM data are formed as multiple tensors to which the proposed data fusion method is applied, followed by a one-class support vector machine for damage detection. The effectiveness of the proposed method has been validated through experiments using synthetic data and data obtained from a real-life bridge. The results have demonstrated that the proposed fusion method is more robust to noise, and able to detect, assess and localize damage better than the use of individual data sources.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Acar, E., Kolda, T.G., Dunlavy, D.M.: All-at-once optimization for coupled matrix and tensor factorizations. In: Proceedings of Mining and Learning with Graphs, MLG 2011, August 2011
Bro, R., Kiers, H.A.L.: A new efficient method for determining the number of components in PARAFAC models. J. Chemometr. 17(5), 274–286 (2003)
Farrar, C.R., Worden, K.: An introduction to structural health monitoring. Philos. Trans. Roy. Soc. A: Math. Phys. Eng. Sci. 365(1851), 303–315 (2007)
Khazai, S., Homayouni, S., Safari, A., Mojaradi, B.: Anomaly detection in hyperspectral images based on an adaptive support vector method. IEEE Geosci. Remote Sens. Lett. 8(4), 646–650 (2011)
Khoa, N.L.D., Anaissi, A., Wang, Y.: Smart infrastructure maintenance using incremental tensor analysis: extended abstract. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, CIKM 2017, pp. 959–967. ACM, New York (2017)
Khoa, N.L.D., et al.: On damage identification in civil structures using tensor analysis. In: Cao, T., Lim, E.-P., Zhou, Z.-H., Ho, T.-B., Cheung, D., Motoda, H. (eds.) PAKDD 2015, Part I. LNCS (LNAI), vol. 9077, pp. 459–471. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-18038-0_36
Kolda, T.G., Bader, B.W.: Tensor decompositions and applications. SIAM Rev. 51(3), 455–500 (2009)
Liu, W., Chan, J., Bailey, J., Leckie, C., Kotagiri, R.: Utilizing common substructures to speedup tensor factorization for mining dynamic graphs. In: Proceedings of the 21st ACM International Conference on Information and Knowledge Management, CIKM 2012, pp. 435–444. ACM, New York (2012)
Nion, D., Sidiropoulos, N.D.: Adaptive algorithms to track the PARAFAC decomposition of a third-order tensor. Trans. Sig. Process. 57(6), 2299–2310 (2009)
Prada, M.A., Toivola, J., Kullaa, J., Hollmén, J.: Three-way analysis of structural health monitoring data. Neurocomputing 80, 119–128 (2012). Special Issue on Machine Learning for Signal Processing 2010
Schölkopf, B., Williamson, R.C., Smola, A.J., Shawe-Taylor, J., Platt, J.C.: Support vector method for novelty detection. In: NIPS, pp. 582–588 (1999)
Sorber, L., Barel, M.V., Lathauwer, L.D.: Structured data fusion. IEEE J. Sel. Top. Sig. Process. 9(4), 586–600 (2015)
Sun, J., Tao, D., Papadimitriou, S., Yu, P.S., Faloutsos, C.: Incremental tensor analysis: theory and applications. ACM Trans. Knowl. Discov. Data 2(3), 11:1–11:37 (2008)
Wang, Y., Wu, L., Lin, X., Gao, J.: Multiview spectral clustering via structured low-rank matrix factorization. IEEE Trans. Neural Netw. Learn. Syst. 29(10), 4833–4843 (2018)
Zhou, S., Vinh, N.X., Bailey, J., Jia, Y., Davidson, I.: Accelerating online cp decompositions for higher order tensors. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016, pp. 1375–1384. ACM, New York (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Khoa, N.L.D., Tian, H., Wang, Y., Chen, F. (2019). Online Data Fusion Using Incremental Tensor Learning. In: Yang, Q., Zhou, ZH., Gong, Z., Zhang, ML., Huang, SJ. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2019. Lecture Notes in Computer Science(), vol 11439. Springer, Cham. https://doi.org/10.1007/978-3-030-16148-4_28
Download citation
DOI: https://doi.org/10.1007/978-3-030-16148-4_28
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-16147-7
Online ISBN: 978-3-030-16148-4
eBook Packages: Computer ScienceComputer Science (R0)