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http://dx.doi.org/10.12989/sss.2022.29.1.093

Data anomaly detection for structural health monitoring of bridges using shapelet transform  

Arul, Monica (Nathaz Modeling Laboratory, Department of Civil & Environmental Engineering & Earth Sciences, University of Notre Dame)
Kareem, Ahsan (Nathaz Modeling Laboratory, Department of Civil & Environmental Engineering & Earth Sciences, University of Notre Dame)
Publication Information
Smart Structures and Systems / v.29, no.1, 2022 , pp. 93-103 More about this Journal
Abstract
With the wider availability of sensor technology through easily affordable sensor devices, several Structural Health Monitoring (SHM) systems are deployed to monitor vital civil infrastructure. The continuous monitoring provides valuable information about the health of the structure that can help provide a decision support system for retrofits and other structural modifications. However, when the sensors are exposed to harsh environmental conditions, the data measured by the SHM systems tend to be affected by multiple anomalies caused by faulty or broken sensors. Given a deluge of high-dimensional data collected continuously over time, research into using machine learning methods to detect anomalies are a topic of great interest to the SHM community. This paper contributes to this effort by proposing a relatively new time series representation named "Shapelet Transform" in combination with a Random Forest classifier to autonomously identify anomalies in SHM data. The shapelet transform is a unique time series representation based solely on the shape of the time series data. Considering the individual characteristics unique to every anomaly, the application of this transform yields a new shape-based feature representation that can be combined with any standard machine learning algorithm to detect anomalous data with no manual intervention. For the present study, the anomaly detection framework consists of three steps: identifying unique shapes from anomalous data, using these shapes to transform the SHM data into a local-shape space and training machine learning algorithms on this transformed data to identify anomalies. The efficacy of this method is demonstrated by the identification of anomalies in acceleration data from an SHM system installed on a long-span bridge in China. The results show that multiple data anomalies in SHM data can be automatically detected with high accuracy using the proposed method.
Keywords
anomaly detection; long-span bridge; machine learning; shapelet transform; structural health monitoring; time series shapelets;
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1 Wan, H.-P. and Ni, Y.-Q. (2018), "Bayesian modeling approach for forecast of structural stress response using structural health monitoring data", J. Struct. Eng., 144(9), 04018130. https://doi.org/10.1061/(ASCE)ST.1943-541X.0002085   DOI
2 Wang, H., Zhang, Y.-M., Mao, J.-X., Wan, H.-P., Tao, T.-Y. and Zhu, Q.-X. (2019), "Modeling and forecasting of temperature-induced strain of a long-span bridge using an improved bayesian dynamic linear model", Eng. Struct., 192, 220-232. https://doi.org/10.1016/j.engstruct.2019.05.006   DOI
3 Xing, Z., Pei, J., Yu, P.S. and Wang, K. (2011), "Extracting interpretable features for early classification on time series", Proceedings of the 2011 SIAM International Conference on Data Mining, Mesa, AZ, USA, April, pp. 247-258. https://doi.org/10.1137/1.9781611972818.22   DOI
4 Bagnall, A., Lines, J., Bostrom, A., Large, J. and Keogh, E. (2017), "The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances", Data Min. Knowl. Discov., 31(3), 606-660. https://doi.org/10.1007/s10618-016-0483-9   DOI
5 Xing, Z., Pei, J. and Philip, S.Y. (2012), "Early classification on time series", Knowl. Inform. Syst., 31(1), 105-127. https://doi.org/10.1007/s10115-011-0400-x   DOI
6 Ye, L. and Keogh, E. (2009), "Time series shapelets: a new primitive for data mining", Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Paris, France, June-July, pp. 947-956. https://doi.org/10.1145/1557019.1557122   DOI
7 Ye, L. and Keogh, E. (2011), "Time series shapelets: a novel technique that allows accurate, interpretable and fast classification", Data Min. Knowl. Discov., 22(1-2), 149-182. https://doi.org/10.1007/s10618-010-0179-5   DOI
8 Hills, J., Lines, J., Baranauskas, E., Mapp, J. and Bagnall, A. (2014), "Classification of time series by shapelet transformation", Data Min. Knowl. Discov., 28(4), 851-881. https://doi.org/10.1007/s10618-013-0322-1   DOI
9 Lines, J., Davis, L.M., Hills, J. and Bagnall, A. (2012), "A shapelet transform for time series classification", Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Beijing, China, August, pp. 289-297. https://doi.org/10.1145/2339530.2339579   DOI
10 McGovern, A., Rosendahl, D.H., Brown, R.A. and Droegemeier, K.K. (2011), "Identifying predictive multidimensional time series motifs: an application to severe weather prediction", Data Min. Knowl. Discov., 22(1-2), 232-258. https://doi.org/10.1007/s10618-010-0193-7   DOI
11 Mao, J., Wang, H. and Spencer Jr, B.F. (2020), "Toward data anomaly detection for automated structural health monitoring: Exploiting generative adversarial nets and autoencoders", Struct. Health Monitor., 20(4), 1609-1626. https://doi.org/10.1177/1475921720924601   DOI
12 Rakthanmanon, T. and Keogh, E. (2013), "Fast shapelets: A scalable algorithm for discovering time series shapelets", Proceedings of the 2013 SIAM International Conference on Data Mining, Austin, TX, USA, May, pp. 668-676. https://doi.org/10.1137/1.9781611972832.74   DOI
13 Tang, Z., Chen, Z., Bao, Y. and Li, H. (2019), "Convolutional neural network-based data anomaly detection method using multiple information for structural health monitoring", Struct. Control Health Monitor., 26(1), e2296. https://doi.org/10.1002/stc.2296   DOI
14 Thiyagarajan, K., Kodagoda, S. and Van Nguyen, L. (2017), "Predictive analytics for detecting sensor failure using autoregressive integrated moving average model", Proceedings of 2017 12th IEEE Conference on Industrial Electronics and Applications (ICIEA), Siem Reap, Cambodia, June, pp, 1926-1931. https://doi.org/10.1109/ICIEA.2017.8283153   DOI
15 Shannon, C.E. and Weaver, W. (1949), The Mathematical Theory of Communication, University of Illinois Press, Urbana, IL, USA, p. 117.
16 Abdelghani, M. and Friswell, M.I. (2004), "Sensor validation for structural systems with additive sensor faults", Struct. Health Monitor., 3(3), 265-275. https://doi.org/10.1177/1475921704045627   DOI
17 Arul, M. and Kareem, A. (2021), "Applications of shapelet transform to time series classification of earthquake, wind and wave data", Eng. Struct., 228, 111564. https://doi.org/10.1016/j.engstruct.2020.111564   DOI
18 Breiman, L. (2001), "Random forests", Mach. Learn., 45(1), 5-32. https://doi.org/10.1023/A:1010933404324   DOI
19 Chang, K.W., Deka, B., Hwu, W.M.W. and Roth, D. (2012), "Efficient pattern-based time series classification on GPU", Proceedings of 2012 IEEE 12th International Conference on Data Mining, Brussels, Belgium, Belgium, pp. 131-140. https://doi.org/10.1109/ICDM.2012.132   DOI
20 Fu, Y., Peng, C., Gomez, F., Narazaki, Y. and Spencer Jr, B.F. (2019), "Sensor fault management techniques for wireless smart sensor networks in structural health monitoring", Struct. Control Health Monitor., 26(7), e2362. https://doi.org/10.1002/stc.2362   DOI
21 Ghalwash, M.F., Radosavljevic, V. and Obradovic, Z. (2013), "Extraction of interpretable multivariate patterns for early diagnostics", Proceedings of 2013 IEEE 13th International Conference on Data Mining, Dallas, TX, USA, December, pp. 201-210. https://doi.org/10.1109/ICDM.2013.19   DOI
22 Hartmann, B. and Link, N. (2010), "Gesture recognition with inertial sensors and optimized dtw prototypes", Proceedings of 2010 IEEE International Conference on Systems, Man and Cybernetics, Istanbul, Turkey, October, pp. 2102-2109. 10.1109/ICSMC.2010.5641703   DOI
23 Bostrom, A. and Bagnall, A. (2017b), "A shapelet transform for multivariate time series classification", arXiv preprint arXiv:1712.06428.
24 Chen, Y., Keogh, E., Hu, B., Begum, N., Bagnall, A., Mueen, A. and Batista, G. (2015), The UCR Time Series Classification Archive. URL: www.cs.ucr.edu/~eamonn/time_series_data/
25 Mueen, A., Keogh, E. and Young, N. (2011), "Logical-shapelets: an expressive primitive for time series classification", Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Diego, CA, USA, August, pp. 1154-1162. https://doi.org/10.1145/2020408.2020587   DOI
26 Bao, Y., Tang, Z., Li, H. and Zhang, Y. (2019), "Computer vision and deep learning-based data anomaly detection method for structural health monitoring", Struct. Health Monitor., 18(2), 401-421. https://doi.org/10.1177/1475921718757405   DOI
27 Bao, Y., Li, J., Nagayama, T., Xu, Y., Spencer Jr., B.F. and Li, H. (2021), "The 1st International Project Competition for Structural Health Monitoring (IPC-SHM, 2020): A summary and benchmark problem", Struct. Health Monitor., 20(4), 2229-2239. https://doi.org/10.1177/14759217211006485   DOI
28 Bostrom, A. and Bagnall, A. (2017a), "Binary shapelet transform for multiclass time series classification", In: Transactions on Large-Scale Data-and Knowledge-Centered Systems XXXII, Springer, pp. 24-46.