Smart Structures and Systems

  • 제29권1호
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  • Pages.53-62
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  • 2022
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  • 1738-1584(pISSN)
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  • 1738-1991(eISSN)
테크노프레스 (Techno-Press)
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DOI QR코드

DOI QR Code

Data anomaly detection for structural health monitoring using a combination network of GANomaly and CNN

  • Liu, Gaoyang (College of Civil Engineering and Architecture, Zhejiang University) ;
  • Niu, Yanbo (College of Civil Engineering and Architecture, Zhejiang University) ;
  • Zhao, Weijian (College of Civil Engineering and Architecture, Zhejiang University) ;
  • Duan, Yuanfeng (College of Civil Engineering and Architecture, Zhejiang University) ;
  • Shu, Jiangpeng (College of Civil Engineering and Architecture, Zhejiang University)
  • 투고 : 2021.04.05
  • 심사 : 2021.07.29
  • 발행 : 2022.01.25
⟨ 이전 논문 다음 논문 ⟩

초록

The deployment of advanced structural health monitoring (SHM) systems in large-scale civil structures collects large amounts of data. Note that these data may contain multiple types of anomalies (e.g., missing, minor, outlier, etc.) caused by harsh environment, sensor faults, transfer omission and other factors. These anomalies seriously affect the evaluation of structural performance. Therefore, the effective analysis and mining of SHM data is an extremely important task. Inspired by the deep learning paradigm, this study develops a novel generative adversarial network (GAN) and convolutional neural network (CNN)-based data anomaly detection approach for SHM. The framework of the proposed approach includes three modules : (a) A three-channel input is established based on fast Fourier transform (FFT) and Gramian angular field (GAF) method; (b) A GANomaly is introduced and trained to extract features from normal samples alone for class-imbalanced problems; (c) Based on the output of GANomaly, a CNN is employed to distinguish the types of anomalies. In addition, a dataset-oriented method (i.e., multistage sampling) is adopted to obtain the optimal sampling ratios between all different samples. The proposed approach is tested with acceleration data from an SHM system of a long-span bridge. The results show that the proposed approach has a higher accuracy in detecting the multi-pattern anomalies of SHM data.

키워드

과제정보

The authors would like to thank the organizations 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 their generously providing the invaluable data from actual structures. 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 on the competition. The authors would like to gratefully acknowledge the National Natural Science Foundation of China (52108179), the China Postdoctoral Science Foundation (2021M692835, 2021M702866).

참고문헌

  1. Akcay, S., Atapour-Abarghouei, A. and Breckon, T.P. (2019), "Ganomaly: Semi-supervised anomaly detection via adversarial training", Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11363 LNCS, pp. 622-637.
  2. 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
  3. Bao, Y., Li, J., Nagayama, T., Xu, Y., Spencer, 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
  4. Barra, S., Carta, S.M., Corriga, A., Podda, A.S. and Recupero, D.R. (2020), "Deep learning and time series-To-image encoding for financial forecasting", IEEE/CAA J. Automat. Sinica, 7(3), 683-692. https://doi.org/10.1109/JAS.2020.1003132
  5. Buda, M., Maki, A. and Mazurowski, M.A. (2018), "A systematic study of the class imbalance problem in convolutional neural networks", Neural Networks, 106, 249-259. https://doi.org/10.1016/j.neunet.2018.07.011
  6. Ding, J., Liu, Y., Zhang, L., Wang, J. and Liu, Y. (2016), "An anomaly detection approach for multiple monitoring data series based on latent correlation probabilistic model", Appl. Intell., 44(2), 340-361. https://doi.org/10.1007/s10489-015-0713-7
  7. Gatti, M. (2019), "Structural health monitoring of an operational bridge: A case study", Eng. Struct., 195, 200-209. https://doi.org/10.1016/j.engstruct.2019.05.102
  8. Gul, M. and Catbas, F.N. (2009), "Statistical pattern recognition for Structural Health Monitoring using time series modeling: Theory and experimental verifications", Mech. Syst. Signal Process., 23(7), 2192-2204. https://doi.org/10.1016/j.ymssp.2009.02.013
  9. Han, J. and Moraga, C. (1995), "The influence of the sigmoid function parameters on the speed of backpropagation learning", Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 930, 195-201. https://doi.org/10.1007/3-540-59497-3_175
  10. He, T. and Li, X. (2019), "Image quality recognition technology based on deep learning", J. Visual Commun. Image Represent., 65, 102654. https://doi.org/10.1016/j.jvcir.2019.102654
  11. He, K., Zhang, X., Ren, S. and Sun, J. (2016), "Deep residual learning for image recognition", Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 770-778.
  12. Heinlein, S., Cawley, P. and Vogt, T. (2019), "Validation of a procedure for the evaluation of the performance of an installed structural health monitoring system", Struct. Health Monitor., 18(5-6), 1557-1568. https://doi.org/10.1177/1475921718798567
  13. 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
  14. Huang, L., Duan, S.W., Zhu, K.M. and Chen, W.G. (2016), "Study on deformation monitoring of subway station deep foundation construction", Appl. Mech. Mater., 847, 425-430. https://doi.org/10.4028/www.scientific.net/AMM.847.425
  15. Isogawa, K., Ida, T., Shiodera, T. and Takeguchi, T. (2018), "Deep shrinkage convolutional neural network for adaptive noise reduction", IEEE Signal Processing Letters, IEEE, 25(2), 224-228. https://doi.org/10.1109/LSP.2017.2782270
  16. Jiang, W., Hong, Y., Zhou, B., He, X. and Cheng, C. (2019), "A GAN-based anomaly detection approach for imbalanced industrial time series", IEEE Access, 7, 143608-143619. https://doi.org/10.1109/ACCESS.2019.2944689
  17. Kerschen, G., De Boe, P., Golinval, J.C. and Worden, K. (2005), "Sensor validation using principal component analysis", Smart Mater. Struct., 14(1), 36-42. https://doi.org/10.1088/0964-1726/14/1/004
  18. Kong, Q., Cao, Y., Iqbal, T., Wang, Y., Wang, W. and Plumbley, M.D. (2020), "Panns: Large-scale pretrained audio neural networks for audio pattern recognition", IEEE/ACM Transactions on Audio Speech and Language Processing, 28, 2880-2894. https://doi.org/10.1109/TASLP.2020.3030497
  19. Li, H. and Ou, J. (2016), "The state of the art in structural health monitoring of cable-stayed bridges", J. Civil Struct. Health Monitor., 6(1), 43-67. https://doi.org/10.1007/s13349-015-0115-x
  20. Mutlu, U. and Alpaydin, E. (2020), "Training bidirectional generative adversarial networks with hints", Pattern Recognition, 103, 107320. https://doi.org/10.1016/j.patcog.2020.107320
  21. Ni, F.T., 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(7), 685-700. https://doi.org/10.1111/mice.12528
  22. Park, J., Kim, K. and Cho, Y.K. (2017), "Framework of automated construction-safety monitoring using cloud-enabled BIM and BLE mobile tracking sensors", J. Constr. Eng. Manag., 143(2), 05016019. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001223
  23. Park, S., Kim, S. and Choi, J.H. (2018), "Gear fault diagnosis using transmission error and ensemble empirical mode decomposition", Mech. Syst. Signal Process., 108, 58-72. https://doi.org/10.1016/j.ymssp.2018.02.028
  24. Radford, A., Metz, L. and Chintala, S. (2016), "Unsupervised representation learning with deep convolutional generative adversarial networks", Proceedings of the 4th International Conference on Learning Representations, ICLR 2016 - Conference Track Proceedings, pp. 1-16.
  25. Rao, A.R.M., Kumar, S.K. and Lakshmi, K. (2014), "A Sensor fault detection algorithm for structural health monitoring using adaptive differential evolution", Int. J. Computat. Methods Eng. Sci. Mech., 15(3), 282-293. https://doi.org/10.1080/15502287.2014.883239
  26. 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), 1-22. https://doi.org/10.1002/stc.2296
  27. Thomas, S., Philipp, S., Sebastian, M.W., Ursula, S.-E. and Georg, L. (2017), "Unsupervised anomaly detection with generative adversarial networks to guide marker discovery", Proceedings of International Conference on Information Processing in Medical Imaging, pp. 146-157.
  28. Tian, L., Fan, Y., Li, L. and Mousseau, N. (2020), "Identifying flow defects in amorphous alloys using machine learning outlier detection methods", Scripta Materialia, 186, 185-189. https://doi.org/10.1016/j.scriptamat.2020.05.038
  29. Xu, Y.L. (2018), "Making good use of structural health monitoring systems of long-span cable-supported bridges", J. Civil Struct. Health Monitor., 8(3), 477-497. https://doi.org/10.1007/s13349-018-0279-2
  30. Xu, Y., Bao, Y., Chen, J., Zuo, W. and Li, H. (2019), "Surface fatigue crack identification in steel box girder of bridges by a deep fusion convolutional neural network based on consumer-grade camera images", Struct. Health Monitor., 18(3), 653-674. https://doi.org/10.1177/1475921718764873
  31. Yang, Y. and Nagarajaiah, S. (2014), "Data compression of structural seismic responses via principled independent component analysis", J. Struct. Eng., 140(7), 04014032. https://doi.org/10.1061/(ASCE)ST.1943-541X.0000946
  32. Yang, C.-L., Chen, Z.-X. and Yang, C.-Y. (2020), "Sensor classification using convolutional neural network by encoding multivariate time series as two-dimensional colored images", Sensors, 20, 168. https://doi.org/10.3390/s20010168
  33. 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(4), 1-21. https://doi.org/10.1142/S0219455416400241
  34. Yin, H. and Gai, K. (2015), "An empirical study on preprocessing high-dimensional class-imbalanced data for classification", Proceedings - 2015 IEEE 17th International Conference on High Performance Computing and Communications, 2015 IEEE 7th International Symposium on Cyberspace Safety and Security and 2015 IEEE 12th International Conference on Embedded Software and Systems, New York, NY, USA, August, pp. 1314-1319. https://doi.org/10.1109/HPCC-CSS-ICESS.2015.205
  35. Yuen, K.V. and Mu, H.Q. (2012), "A novel probabilistic method for robust parametric identification and outlier detection", Probabil. Eng. Mech., 30, 48-59. https://doi.org/10.1016/j.probengmech.2012.06.002