Smart Structures and Systems

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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)
  • Received : 2021.04.13
  • Accepted : 2021.07.29
  • Published : 2022.01.25
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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

Acknowledgement

This work was supported in part by the Robert M. Moran Professorship and National Science Foundation Grant (CMMI 1612843). The authors would like to thank the organizers of the International Project Competition for Structural Health Monitoring (IPC-SHM 2020), Asia-Pacific Network of Centers for Research in Smart Structures Technology (ANCRiSST), Harbin Institute of Technology (China), and the University of Illinois at Urbana-Champaign (USA) for providing the structural health monitoring data of the long-span bridge. The authors also would like to thank the chairs of IPC-SHM 2020, Prof. Hui Li and Prof. Billie F. Spencer Jr, for their leadership in the competition.

References

  1. 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
  2. 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
  3. 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
  4. 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
  5. 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
  6. 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.
  7. Bostrom, A. and Bagnall, A. (2017b), "A shapelet transform for multivariate time series classification", arXiv preprint arXiv:1712.06428.
  8. Breiman, L. (2001), "Random forests", Mach. Learn., 45(1), 5-32. https://doi.org/10.1023/A:1010933404324
  9. 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
  10. 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/
  11. 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
  12. 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
  13. 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
  14. 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
  15. 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
  16. 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
  17. 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
  18. 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
  19. 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
  20. Shannon, C.E. and Weaver, W. (1949), The Mathematical Theory of Communication, University of Illinois Press, Urbana, IL, USA, p. 117.
  21. 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
  22. 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
  23. 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
  24. 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
  25. 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
  26. 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
  27. 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
  28. 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