Smart Structures and Systems

  • 제29권1호
  • /
  • Pages.181-193
  • /
  • 2022
  • /
  • 1738-1584(pISSN)
  • /
  • 1738-1991(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

CNN based data anomaly detection using multi-channel imagery for structural health monitoring

  • Shajihan, Shaik Althaf V. (Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign) ;
  • Wang, Shuo (Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign) ;
  • Zhai, Guanghao (Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign) ;
  • Spencer, Billie F. Jr. (Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign)
  • 투고 : 2021.04.28
  • 심사 : 2021.09.24
  • 발행 : 2022.01.25
⟨ 이전 논문 다음 논문 ⟩

초록

Data-driven structural health monitoring (SHM) of civil infrastructure can be used to continuously assess the state of a structure, allowing preemptive safety measures to be carried out. Long-term monitoring of large-scale civil infrastructure often involves data-collection using a network of numerous sensors of various types. Malfunctioning sensors in the network are common, which can disrupt the condition assessment and even lead to false-negative indications of damage. The overwhelming size of the data collected renders manual approaches to ensure data quality intractable. The task of detecting and classifying an anomaly in the raw data is non-trivial. We propose an approach to automate this task, improving upon the previously developed technique of image-based pre-processing on one-dimensional (1D) data by enriching the features of the neural network input data with multiple channels. In particular, feature engineering is employed to convert the measured time histories into a 3-channel image comprised of (i) the time history, (ii) the spectrogram, and (iii) the probability density function representation of the signal. To demonstrate this approach, a CNN model is designed and trained on a dataset consisting of acceleration records of sensors installed on a long-span bridge, with the goal of fault detection and classification. The effect of imbalance in anomaly patterns observed is studied to better account for unseen test cases. The proposed framework achieves high overall accuracy and recall even when tested on an unseen dataset that is much larger than the samples used for training, offering a viable solution for implementation on full-scale structures where limited labeled-training data is available.

키워드

과제정보

The authors would like to thank the organizers of the International Project Competition for SHM (IPC-SHM 2020), ANCRiSST, Harbin Institute of Technology (China), and University of Illinois at Urbana-Champaign (USA) for generously providing the data used in this study. We gratefully acknowledge the guidance and constructive criticism offered by Dr. Yasutaka Narazaki, Zhejiang University-UIUC Institute throughout this study. Additionally, the second and third authors acknowledge the partial support of this research by the China Scholarship Council.

참고문헌

  1. 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
  2. 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
  3. Friswell, M.I. and Inman, D.J. (1999), "Sensor validation for smart structures", J. Intell. Mater. Syst. Struct., 10(12), 973-982. https://doi.org/10.1106/GVD2-EGPN-C5B1-DPNX
  4. 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
  5. Hernandez-Garcia, M.R. and Masri, S.F. (2014), "Application of statistical monitoring using latent-variable techniques for detection of faults in sensor networks", J. Intell. Mater. Syst. Struct., 25(2), 121-136. https://doi.org/10.1177/1045389X13479182
  6. Ibarguengoytia, P.H., Sucar, L.E. and Vadera, S. (2001), "Real time intelligent sensor validation", IEEE Transact. Power Syst., 16(4), 770-775. https://doi.org/10.1109/59.962425
  7. Im, J., Park, H. and Takeuchi, W. (2020), "Advances in remote sensing-based disaster monitoring and assessment", Remote Sensing, 11(18), 2181. https://doi.org/10.3390/rs11182181
  8. Ioannou, Y. (2017), "A Tutorial on Filter Groups (Grouped Convolution)", A Shallow Blog about Deep Learning. https://blog.yani.ai/filter-group-tutorial/, Accessed 04/24/2021
  9. Kampffmeyer, M., Salberg, A.-B. and Jenssen, R. (2016), "Semantic segmentation of small objects and modeling of uncertainty in urban remote sensing images using deep convolutional neural networks", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Las Vegas, NA, USA, June.
  10. Kerschen, G., Boe, P.D., Golinval, J.-C. and Worden, K. (2004), "Sensor validation using principal component analysis", Smart Mater. Struct., 14(1), 36-42. https://doi.org/10.1088/0964-1726/14/1/004
  11. Krawczyk, B. (2016), "Learning from imbalanced data: open challenges and future directions", Progress Artif. Intell., 5, 221-232. https://doi.org/10.1007/s13748-016-0094-0
  12. Krizhevsky, A., Sutskever, I. and Hinton, G.E. (2012), "Imagenet classification with deep convolutional neural networks", Proceedings of the Advances in Neural Information Processing Systems, Lake Tahoe, USA, December.
  13. Kullaa, J. (2010), "Sensor validation using minimum mean square error estimation", Mech. Syst. Signal Process., 24(5), 1444-1457. https://doi.org/10.1016/j.ymssp.2009.12.001
  14. Li, L., Liu, G., Zhang, L. and Li, Q. (2019), "Sensor fault detection with generalized likelihood ratio and correlation coefficient for bridge SHM", J. Sound Vib., 442, 445-458. https://doi.org/10.1016/j.jsv.2018.10.062
  15. Nagarajaiah, S. and Yang, Y. (2017), "Modeling and harnessing sparse and low-rank data structure: a new paradigm for structural dynamics, identification, damage detection, and health monitoring", Struct. Control Health Monitor., 24, e1851. https://doi.org/10.1002/stc.1851
  16. Ni, F., Zhang, J. and Noori, M.N. (2020), "Deep learning for data anomaly detection and data compression of a long-span suspension bridge", Comput.-Aided Civil Infrastr. Eng., 35, 685-700. https://doi.org/10.1111/mice.12528
  17. Oh, B.K., Glisic, B., Kim, Y. and Park, H.S. (2020), "Convolutional neural network-based data recovery method for structural health monitoring", Struct. Health Monitor., 19(6), 1821-1838. https://doi.org/10.1177/1475921719897571
  18. Ou, J. and Li, H. (2009), Structural Health Monitoring of Civil Infrastructure Systems, Chapter 15. Structural health monitoring research in China: trends and applications), Woodhead Publishing Limited, Sawston, Cambridge, UK.
  19. Smarsly, K. and Law, K.H. (2014), "Decentralized fault detection and isolation in wireless structural health monitoring systems using analytical redundancy", Adv. Eng. Software, 73, 1-10. https://doi.org/10.1016/j.advengsoft.2014.02.005
  20. Sohn, H., Farrar, C.R., Hemez, F.M. and Czarnecki, J.J. (2002), "A Review of Structural Health Review of Structural Health Monitoring Literature 1996-2001", Proceedings of the 3rd World Conference on Structural Control, Como, Italy, April.
  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. Yang, Y. and Nagarajaiah, S. (2016), "Harnessing data structure for recovery of randomly missing structural vibration responses time history: Sparse representation versus low-rank structure", Mech. Syst. Signal Process., 74, 165-182. https://doi.org/10.1016/j.ymssp.2015.11.009
  23. Yi, T.-H., Li, H.-N., Song, G. and Guo, Q. (2016), "Detection of shifts in GPS measurements for a long-span bridge using CUSUM chart", Int. J. Struct. Stabil. Dyn., 16(04). https://doi.org/10.1142/S0219455416400241
  24. Yi, T.-H., Huang, H.-B. and Li, H.-N. (2017), "Development of sensor validation methodologies for structural health monitoring: A comprehensive review", Measurement, 109, 200-214. https://doi.org/10.1016/j.measurement.2017.05.064
  25. Zhang, Y. and Lei, Y. (2021), "Data Anomaly Detection of Bridge Structures Using Convolutional Neural Network Based on Structural Vibration Signals", Symm. Struct. Health Monitor., 13(7), 1186. https://doi.org/10.3390/sym13071186