Abstract
Structural health monitoring (SHM) includes both static and highly dynamic engineering systems. With the advent of real-time sensing, edge-computing, and high-bandwidth computer memory, there is an ability to enable high-rate SHM (HR-SHM). The paper defines the technical area of high-rate structural health monitoring and prognostics and presents the HR-SHM technical grand challenges including multi-timescales of the problem, adequate sensor network and response, real-time assessment, and decision-making with quantified uncertainty and risk. Key issues to address in such challenges include the time duration of the event, timescales of the physics, multiple sources of uncertainty, as well as limited spatiotemporal constraints for hardware execution. The paper defines the high-rate timescale as 1 ms on the integrated paradigm including data acquisition, assessment execution, and decision-making. The spatial issues include the resolution of the area monitored, the communication distance, and the number of edge sensors. The temporal issue includes the sensor type (e.g., THz) as well as multiple sources of uncertainty. These constraints must be coupled to allow for high-rate implementation that is robust, adaptable, and beneficial to the missions of interest. To address the grand challenge, we propose physics-informed real-time fusion (PIRF) of high-speed dynamic data. Technologies such as machine learning and edge-computing can be further harnessed to enable structural and functional prognostics for high-rate dynamic systems. Quantification of uncertainty, both aleatory and epistemic, is necessary for real-time state estimation to be connected with the confidences to integrate risks into the decision-making.
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References
Farrar, C., Worden, K.: An introduction to structural health monitoring. Phil. Trans. R. Soc. A. 365 (2007). https://doi.org/10.1098/rsta.2006.1928
Seo, J., Hu, J.W., Lee, J.: Summary review of structural health monitoring applications for highway bridges. J. Perform. Constr. Facil. 30(4), 04015072 (2016)
Chang, F.K.: Structural Health Monitoring 2013: A Roadmap to Intelligent Structures. DESTech, Lancaster (2013)
Blasch, E., Ravela, S., Aved, A. (eds.): Handbook of Dynamic Data Driven Applications Systems. Springer, Cham (2018)
Wadley, H.N., Dharmasena, K.P., He, M., McMeeking, R.M., Evans, A.G., Bui-Thanh, T., Radovitzky, R.: An active concept for limiting injuries caused by air blasts. Int. J. Impact Eng. 37, 317–323 (2010)
Lee, S.J., Jang, M.S., Kim, Y.G., Park, G.T.: Stereovision-based real-time occupant classification system for advanced airbag systems. Int. J. Automot. Technol. 12, 425–432 (2011)
Hong, J., Laflamme, S., Dodson, J., Joyce, B.: Introduction to state estimation of high-rate system dynamics. Sensors. 18(2), 217 (2018)
Blasch, E., Bosse, E., Lambert, D.A.: High-level Information Fusion Management and Systems Design. Artech House, Norwood, MA (2012)
Blasch, E., Liu, S., Liu, Z., Zheng, Y.: Deep Learning Measures of Effectiveness. IEEE National Aerospace and Electronics Systems Conference (2018)
Blasch, E., Pokines, B.: Analytical Science for Autonomy Evaluation. IEEE National Aerospace and Electronics Systems Conference. (2019)
Majumder, U., Blasch, E., Garren, D.: Deep Learning for Radar and Communications Automatic Target Recognition. Artech House, Norwood (2020)
Blasch, E., Tiley, J.S., Sparkman, D., Donegan, S., Cherry, M.: Data fusion methods for materials awareness. Proc. SPIE. 11423, 114230K (2020)
Darema, F., Blasch, E., Ravela, S., Aved, A. (eds). InfoSymbiotics/Dynamic Data Driven Applications Systems Conference (Spring, 2020)
Udea, K., Umeda, A.: Dynamic response of strain gages up to 300kHz. Exp. Mech. 38, 93 (1998)
Downey, A., Hong, J., Dodson, J., Carroll, M., Scheppegrell, J.: Millisecond model updating for structures experiencing unmodeled high-rate dynamic events. Mech. Syst. Signal Process. 138, 106551 (2020)
Zheng, Y., Blasch, E., Liu, Z.: Multispectral Image Fusion and Colorization. SPIE Press, Bellingham (2018)
Snidaro, L., Garcia, J., Llinas, L., et al. (eds.): Context-Enhanced Information Fusion: Boosting Real-World Performance with Domain Knowledge. Springer, Cham (2016)
Ma, M., Mao, Z.: Deep-convolution-based LSTM network for remaining useful life prediction. IEEE Trans. Ind. Inf. 17(3), 1658–1667 (2021)
Ma, M., Mao, Z.: Deep wavelet sequence-based gated recurrent units for the prognosis of rotating machinery. In: Structural Health Monitoring (in press, 2020)
Barzegar, V., Laflamme, S., Hu, C., Dodson, J.: Ensemble of Recurrent Neural Networks with Long Short-Term Memory Cells for High-Rate Structural Health Monitoring. Mechanical Systems and Signal Processing (2021)
Qian, E., Kramer, B., Peherstorfer, B., Willcox, K.: Lift & learn: physics-informed machine learning for large-scale nonlinear dynamical systems. Physica D Nonlinear Phenomena. 406, 132401 (2020)
Kapteyn, M. I. G., Willcox, K. E.: From physics-based models to predictive digital twins via interpretable machine learning, 2020. arXiv preprint arXiv:2004.11356
Todd, M.D., Leung, M., J Corcoran.: A Probability Density Function for Uncertainty Quantification in the Failure Forecast Method. Proceedings of the 9th European Workshop on Structural Health Monitoring (2018)
Leung, M.S.H., Corcoran, J., Cawley, P., Todd, M.D.: Evaluating the use of rate-based monitoring for improved fatigue remnant life predictions. Int. J. Fatigue. 120, 162–174 (2019)
Joyce, B., Dodson, J., Laflamme, S., Hong, J.: An experimental test bed for developing high-rate structural health monitoring methods. Shock. Vib. (2018)
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Dodson, J. et al. (2022). High-Rate Structural Health Monitoring and Prognostics: An Overview. In: Madarshahian, R., Hemez, F. (eds) Data Science in Engineering, Volume 9. Conference Proceedings of the Society for Experimental Mechanics Series. Springer, Cham. https://doi.org/10.1007/978-3-030-76004-5_23
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DOI: https://doi.org/10.1007/978-3-030-76004-5_23
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