Hybrid Methodology for Structural Health Monitoring Based on Immune Algorithms and Symbolic Time Series Analysis


This hybrid methodology for structural health monitoring (SHM) is based on immune algorithms (IAs) and symbolic time series analysis (STSA). Real-valued negative selection (RNS) is used to detect damage detection and adaptive immune clonal selection algorithm (AICSA) is used to localize and quantify the damage. Data symbolization by using STSA alleviates the effects of harmful noise in raw acceleration data. This paper explains the mathematical basis of STSA and the procedure of the hybrid methodology. It also describes the results of an simulation experiment on a five-story shear frame structure that indicated the hybrid strategy can efficiently and precisely detect, localize and quantify damage to civil engineering structures in the presence of measurement noise.

Share and Cite:

R. Li, A. Mita and J. Zhou, "Hybrid Methodology for Structural Health Monitoring Based on Immune Algorithms and Symbolic Time Series Analysis," Journal of Intelligent Learning Systems and Applications, Vol. 5 No. 1, 2013, pp. 48-56. doi: 10.4236/jilsa.2013.51006.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] R. I. Levin and N. A. J. Lieven, “Dynamic Finite Element Model Updating Using Simulated Annealing and Genetic Algorithms,” Mechanical System and Signal Process, Vol. 12, No. 1, 1998, pp. 91-120. doi:10.1006/mssp.1996.0136
[2] K. Cunha and V. V. Smith, “A Determination of the Solar Photospheric Boron Abundance,” Astrophysical Journal, Vol. 512, No. 2, 1999, pp. 1006-1013. doi:10.1086/306796
[3] S. T. Xue, H. S. Tang and J. Zhou, “Identification of Structural Systems Using Particle Swarm Optimization,” Journal of Asian Architecture and Building Engineering, Vol. 8, No. 2, 2009, pp. 517-524. doi:10.3130/jaabe.8.517
[4] R. Li and A. Mita, “Structural Damage Identification Using Adaptive Immune Clonal Selection Algorithm and Acceleration Data,” SPIE Smart Structures/NDE2011, San Diego, 6-10 March, 2011, 79815A.
[5] R. Li and A. Mita, “Hybrid Immune Algorithm for Structural Health Monitoring Using Acceleration Data,” 8th International Workshop on Structural Health Monitoring, Stanford University, 13-15 September 2011, pp. 1095-1102.
[6] A. Ray, “Symbolic Dynamic Analysis of Complex Systems for Anomaly Detection,” Signal Processing, Vol. 84, No. 7, 2004, pp. 1115-1130. doi:10.1016/j.sigpro.2004.03.011
[7] S. Chin, A. Ray and V. Rajagopalan, “Symbolic Time Series Analysis for Anomaly Detection: A Comparative Evaluation,” Signal Processing, Vol. 85, No. 9, 2005, pp. 1859-1868. doi:10.1016/j.sigpro.2005.03.014
[8] A. Khatkhate, A. Ray, S. Chin, V. Rajagopalan and E. Keller, “Detection of Fatigue Crack Anomaly: A Symbolic Dynamic Approach,” Proceedings of American Control Conference, Boston, 30 June-2 July 2004; pp. 3741-3746.
[9] D. Tolani, M. Yasar, A. Ray and V. Yang, “Anomaly Detection in Aircraft Gas Turbine Engines,” AIAA Journal of Aerospace Computing Information, and Communication, Vol. 3, No. 2, 2006, pp. 44-51. doi:10.2514/1.15768
[10] S. Bhatnagar, V. Rajagopalan and A. Ray, “Incipient Fault Detection in Mechanical Power Transmission Systems,” Proceedings of American Control Conference, Portland, 8-10 June 2005, pp. 472-477.
[11] S. Forrest, A. Perelson, L. Allen and R. Cherukuri, “SelfNonself Discrimination in a Computer’,” Proceedings of IEEE Symposium on Research in Security and Privacy, Los Alamitos, 16-18 May 1994, pp. 202-212.
[12] F. Gonzalez, D. Dasgupta and R. Kozma, “Combining Negative Selection and Classification Techniques for Anomaly Detection,” Proceedings of the 2002 Congress on Evolutionary Computation CEC2002, 12-17 May Honolulu, pp. 705-710.
[13] F. A. Gonzalez and D. Dasgupta, “Anomaly Detection Using Real-Valued Negative Selection,” Genetic Programming and Evolvable Machines, Vol. 4, No. 4, 2003, pp. 383-403.
[14] X. Wang, X. Z. Gao and S. J. Ovaska, “Artificial Immune Optimization Methods and Applications: A Survey,” Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, The Hague, 10-13 October 2004, pp. 3415-3420.
[15] J. Timmis, P. Andrews, N. Owens and E. Clark, “An Interdisciplinary Perspective on Artificial Immune Systems,” Evolutionary Intelligence, Vol. 1, No. 1, 2008. pp. 5-26. doi:10.1007/s12065-007-0004-2
[16] L. Zhang, X. R. Meng, W. J. Wu and H. Zhou, “Network Fault Feature Selection Based on Adaptive Immune Clonal Selection Algorithm,” 2009 International Joint Conference on Computational Sciences and Optimization, Sanya, 24-26 April 2009, pp. 969-973. doi:10.1109/CSO.2009.342
[17] A. Mita, “Structural Dynamics for Health Monitoring,” Sankeisha Co., Ltd, Nagoya, 2003.
[18] R. S. Li, A. Mita and J. Zhou, “Feasibility Study of Parameter Identification Method Based on Symbolic Time Series Analysis and Adaptive Immune Clonal Selection Algorithm,” Open Journal of Civil Engineering, Vol. 2 No. 4, 2012, pp. 198-205.

Copyright © 2020 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.