Smart Structures and Systems

  • 제29권1호
  • /
  • Pages.77-91
  • /
  • 2022
  • /
  • 1738-1584(pISSN)
  • /
  • 1738-1991(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

SHM data anomaly classification using machine learning strategies: A comparative study

  • Chou, Jau-Yu (Department of Civil Engineering, National Taiwan University) ;
  • Fu, Yuguang (School of Civil and Environmental Engineering, Nanyang Technological University) ;
  • Huang, Shieh-Kung (Department of Civil Engineering, National Chung Hsing University) ;
  • Chang, Chia-Ming (Department of Civil Engineering, National Taiwan University)
  • 투고 : 2021.04.12
  • 심사 : 2021.08.03
  • 발행 : 2022.01.25
⟨ 이전 논문 다음 논문 ⟩

초록

Various monitoring systems have been implemented in civil infrastructure to ensure structural safety and integrity. In long-term monitoring, these systems generate a large amount of data, where anomalies are not unusual and can pose unique challenges for structural health monitoring applications, such as system identification and damage detection. Therefore, developing efficient techniques is quite essential to recognize the anomalies in monitoring data. In this study, several machine learning techniques are explored and implemented to detect and classify various types of data anomalies. A field dataset, which consists of one month long acceleration data obtained from a long-span cable-stayed bridge in China, is employed to examine the machine learning techniques for automated data anomaly detection. These techniques include the statistic-based pattern recognition network, spectrogram-based convolutional neural network, image-based time history convolutional neural network, image-based time-frequency hybrid convolution neural network (GoogLeNet), and proposed ensemble neural network model. The ensemble model deliberately combines different machine learning models to enhance anomaly classification performance. The results show that all these techniques can successfully detect and classify six types of data anomalies (i.e., missing, minor, outlier, square, trend, drift). Moreover, both image-based time history convolutional neural network and GoogLeNet are further investigated for the capability of autonomous online anomaly classification and found to effectively classify anomalies with decent performance. As seen in comparison with accuracy, the proposed ensemble neural network model outperforms the other three machine learning techniques. This study also evaluates the proposed ensemble neural network model to a blind test dataset. As found in the results, this ensemble model is effective for data anomaly detection and applicable for the signal characteristics changing over time.

키워드

과제정보

The structural health monitoring data of the long-span bridge are obtained from the organizers of the 1st International Project Competition for Structural Health Monitoring (IPC-SHM), 2020 (http://www.schm.org.cn/#/IPC-SHM, 2020).

참고문헌

  1. Bao, Y., Chen Z., Wei, S., Xu, Y., Tang, Z. and Li, H. (2019a), "The state of the art of data science and engineering in structural health monitoring", Engineering, 5, 234-242. https://doi.org/10.1016/j.eng.2018.11.027
  2. Bao, Y., Tang, Z., Li, H. and Zhang, Y. (2019b), "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. Boashash, B. and Ouelha, S. (2016), "Automatic signal abnormality detection using time-frequency features and machine learning: A newborn EEG seizure case study", Knowledge-Based Syst., 106, 38-50. https://doi.org/10.1016/j.knosys.2016.05.027
  5. Chang, C.M., Chou, J.Y., Tan, P. and Wang, L. (2017), "A sensor fault detection strategy for structural health monitoring systems", Smart Struct. Syst., Int. J., 20(1), 43-52. https://doi.org/10.12989/sss.2017.20.1.043
  6. Christian, S., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V. and Rabinovich, A. (2015), "Going deeper with convolutions", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-9.
  7. Dragos, K. and Smarsly, K. (2016), "Distributed adaptive diagnosis of sensor faults using structural response data", Smart Mater. Struct., 25(10), 105019. https://doi.org/10.1088/0964-1726/25/10/105019
  8. 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
  9. Huang, H.B., Yi, T.H. and Li, H.N. (2017a), "Sensor fault diagnosis for structural health monitoring based on statistical hypothesis test and missing variable approach", J. Aerosp. Eng., 30(2), B4015003. https://doi.org/10.1061/(ASCE)AS.1943-5525.0000572
  10. Huang, H.B., Yi, T.H. and Li, H.N. (2017b), "Bayesian combination of weighted principal-component analysis for diagnosing sensor faults in structural monitoring systems", J. Eng. Mech., 143(9), 04017088. https://doi.org/10.1061/(ASCE)EM.1943-7889.0001309
  11. Krizhevesky, A., Sutskever, I. and Hinton, G.E. (2012), "ImageNet classification with deep convolutional neural networks", Adv. Neural Inform. Process. Syst., 25.
  12. 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.ymssp.2015.05.011
  13. Lo, C., Lynch, J.P. and Liu, M. (2016), "Distributed model-based nonlinear sensor fault diagnosis in wireless sensor networks", Mech. Syst. Signal Process., 66, 470-484. https://doi.org/10.1016/j.ymssp.2015.05.011
  14. Mahapatro, A. and Khilar, P.M. (2013), "Fault diagnosis in wireless sensor networks: A survey", IEEE Commun. Surveys Tutorials, 15(4), 2000-2026. https://doi.org/10.1109/SURV.2013.030713.00062
  15. 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
  16. MATLAB and Deep Learning Toolbox Release (2020b), The MathWorks, Inc., Natick, MA, USA.
  17. Munir, M., Siddiqui, S.A., Chattha, M.A., Dengel, A. and Ahmed, S. (2019), "FuseAD: unsupervised anomaly detection in streaming sensors data by fusing statistical and deep learning models", Sensors, 19(11), 2451. https://doi.org/10.3390/s19112451
  18. Pan, S.J. and Yang, Q. (2010), "A survey on transfer learning", IEEE Transact. Knowledge Data Eng., 22(10), 1345-1359. https://doi.org/10.1109/TKDE.2009.191
  19. Pavelka, A. and Proch, A. (2004), "Algorithm for initialization of neural network weights random numbers in MATLAB", Proceeding: Control Engineering, 2, 453-459.
  20. Peng, C., Fu, Y. and Spencer Jr., B.F. (2017a), "Sensor fault detection, identification, and recovery techniques for wireless sensor networks: A full-scale study", Proceedings of the 13th International Workshop on Advanced Smart Materials and Smart Structures Technology.
  21. Peng, Y., Qiao, W., Qu, L. and Wang, J. (2017b), "Sensor fault detection and isolation for a wireless sensor network-based remote wind turbine condition monitoring system", IEEE Transact. Ind. Applicat., 54(2), 1072-1079. https://doi.org/10.1109/TIA.2017.2777925
  22. Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C. and Li, F.F. (2015), "ImageNet larger scale visual recognition challenge", Int. J. Comput. Vision, 115, 211-252. https://doi.org/10.1007/s11263-015-0816-y
  23. Sharma, A.B., Golubchik, L. and Govindan, R. (2010), "Sensor faults: detection methods and prevalence in real-world datasets", ACM Transact. Sensor Networks (TOSN), 6(3), 23. https://doi.org/10.1145/1754414.1754419
  24. Singla, A., Yuan, L. and Ebrahimi, T. (2016), "Food/non-food image classification and food categorization using pre-trained GoogLeNet Model", MADiMa 16: Proceedings of the 2nd International Workshop on Multimedia Assisted Dietary Management, October, pp. 3-11. https://doi.org/10.1145/2986035.2986039
  25. 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
  26. Tang, Z., Chen, Z., Bao, Y. and Li, H. (2018), "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
  27. Yu, C.B., Hu, J.J., Li, R., Deng, S.H. and Yang, R.M. (2014), "Node fault diagnosis in WSN based on RS and SVM", Proceedings of 2014 International Conference on Wireless Communication and Sensor Network, Wuhan, China, December, pp. 153-156.
  28. Zhao, C., Sun, X., Sun, S. and Jiang, T. (2011), "Fault diagnosis of sensor by chaos particle swarm optimization algorithm and support vector machine", Expert Syst. Applicat., 38(8), 9908-9912. https://doi.org/10.1016/j.eswa.2011.02.043