Recent sensing advances empower the deployment of dense sensor networks (DSNs) that can be used in automating the condition assessment process of large-scale structural and mechanical systems. To fully enable DSN technologies, it is critical to develop and implement co-design techniques that allow for the evaluation of their performance to attain the condition assessment targets. In this paper, we propose a technique to validate the design of DSNs. The technique consists of constructing a physical surrogate of the DSN equipped component based on the sensor configuration, updating the physical surrogate using the sliding mode theory based on generated or collected sensor data, and quantifying the performance of the DSN based on model-assisted probability of detection theory. The proposed technique is numerically verified and validated on a cantilever beam subjected to damage at its root and equipped with a network of soft elastomeric capacitors measuring strain. Various uncertainties are considered in the simulated system. The verification consists of confirming the capability of the adaptive process for the surrogate model at reaching an accurate representation of the full system. The validation consists of ranking the performance of various DSN configuration and benchmarking results against those obtained from the full finite element model. Results show that the proposed technique can be used to evaluate the performance of DSN configurations, but that the damage thresholds used in determining damage need to be standardized for successful field applications.
Aldrin, J.C., E.A. Medina, E.A. Lindgren, C.F. Buynak, and J.S Knopp, 2011, “Case Studies for Model-Assisted Probabilistic Reliability Assessment for Structural Health Monitoring,” AIP Conference Proceedings, Vol. 1335, pp. 1589–1596.
Amsallem, D., and C. Farhat, 2008, “Interpolation Method for Adapting Reduced-Order Models and Application to Aeroelasticity,” AIAA Journal, Vol. 46, No. 7, pp. 1803–1813.
Bartel, T., 2005, “Uncertainty in NIST Force Measurements,” Journal of Research of the National Institute of Standards and Technology, Vol. 110, No. 6, pp. 589–603.
Benner, P., A. Cohen, M. Ohlberger, and K. Wilcox, 2017, Model Reduction and Approximation: Theory and Algorithms, SIAM-Society for Industrial & Applied Mathematics, Philadelphia, PA.
Benner, P., S. Gugercin, and K. Willcox, 2015, “A Survey of Projection-Based Model Reduction Methods for Parametric Dynamical Systems,” SIAM Review, Vol. 57, No. 4, pp. 483–531.
Cancelli, A., S. Laflamme, A. Alipour, S. Sritharan, and F. Ubertini, 2019, “Vibration-Based Damage Localization and Quantification in a Pretensioned Concrete Girder Using Stochastic Subspace Identification and Particle Swarm Model Updating,” Structural Health Monitoring, p. 147592171882001.
Cui, R., and F. Lanza di Scalea, 2019, “On the Identification of the Elastic Properties of Composites by Ultrasonic Guided Waves and Optimization Algorithm,” Composite Structures, Vol. 223, doi: 10.1016/j.compstruct.2019.110969.
Downey, A., M. Sadoughi, S. Laflamme, and C. Hu, 2018b, “Incipient Damage Detection for Large Area Structures Monitored with a Network of Soft Elastomeric Capacitors Using Relative Entropy” IEEE Sensors Journal, Vol. 18, No. 21, pp. 8827–8834.
Downey, A., M. Sadoughi, S. Laflamme, and C.H. Hu, 2018a, “Fusion of Sensor Geometry into Additive Strain Fields Measured with Sensing Skin,” Smart Materials and Structures, Vol. 27, No. 7, doi: 10.1088/1361-665X/aac4cd.
Downey, A., Y.-H. Lui, C. Hu, S. Laflamme, and S. Hu, 2019, “Physics-Based Prognostics of Lithium-Ion Battery Using Non-Linear Least Squares with Dynamic Bounds,” Reliability Engineering & System Safety, Vol. 182, pp. 1–12.
Du, X., J. Yan, S. Laflamme, L. Leifsson, Y. Tesfahunegn, and S. Koziel, 2018b, “Model-Assisted Probability of Detection for Structural Health Monitoring of Flat Plates,” Computational Science – ICCS 2018, Vol. 10861, pp. 618–628.
Du, X., P. Gurrala, L. Leifsson, J. Song, W.Q. Meeker, R.A. Roberts, S. Koziel, and Y.A. Tesfahunegn, 2018a, Stochastic-Expansions-Based Model-Assisted Probability of Detection Analysis of the Spherically-Void-Defect Benchmark Problem,” Computational Science – ICCS 2018, Vol. 10861, pp. 593–603.
Forsyth, D.S., 2016, “Structural Health Monitoring and Probability of Detection Estimation,” AIP Conference Proceedings, Vol. 1706, No. 1, doi: 10.1063/1.4940648.
Haddad, R.E., R. Fakhereddine, C. Lécot, and G. Venkiteswaran, 2013, “Extended Latin Hypercube Sampling for Integration and Simulation,” Monte Carlo and Quasi-Monte Carlo Methods 2012, pp. 317–330.
Joyce, B., J.C. Dodson, S. Laflamme, and J. Hong, 2018a, “An Experimental Test Bed for Developing High-Rate Structural Health Monitoring Methods,” Shock and Vibration, Vol. 2018, doi: 10.1155/2018/3827463.
Joyce, B.S., J. Hong, J.C. Dodson, J.C. Wolfson, and S. Laflamme, 2018b, “Adaptive Observers for Structural Health Monitoring of High-Rate, Time-Varying Dynamic Systems,” Structural Health Monitoring, Photogrammetry & DIC, Vol. 6, pp. 109–119.
Laflamme, S., F. Ubertini, H. Saleem, A. D’Alessandro, A. Downey, H. Ceylan, and A.L. Materazzi, 2015, “Dynamic Characterization of a Soft Elastomeric Capacitor for Structural Health Monitoring,” Journal of Structural Engineering, Vol. 141, No. 8, doi: 10.1061/(ASCE)ST.1943-541X.0001151.
Laflamme, S., H.S. Saleem, B.K. Vasan, R.L. Geiger, D. Chen, M.R. Kessler, and K. Rajan, 2013, “Soft Elastomeric Capacitor Network for Strain Sensing Over Large Surfaces,” IEEE/ASME Transactions on Mechatronics, Vol. 18, No. 6, pp. 1647–1654.
Le Gratiet, L., B. Iooss, G. Blatman, T. Browne, S. Cordeiro, and B. Goursaud, 2016, “Model Assisted Probability of Detection Curves: New Statistical Tools and Progressive Methodology,” Journal of Nondestructive Evaluation, Vol. 36, No. 8, doi: 10.1007/s10921-016-0387-z.
Li, D., X. Dong, and Y. Wang, 2018, “Model Updating Using Sum of Squares (SOS) Optimization to Minimize Modal Dynamic Residuals,” Structural Control and Health Monitoring, Vol. 25, No. 12, doi: 10.1002/stc.2263.
Liao, Y., A.S. Kiremidjian, R. Rajagopal, and C.-H. Loh, 2019, “Structural Damage Detection and Localization with Unknown Postdamage Feature Distribution Using Sequential Change-Point Detection Method,” Journal of Aerospace Engineering, Vol. 32, No. 2, doi: 10.1061/(ASCE)AS.1943-5525.0000979.
Lu, Y., and J.E. Michaels, 2009, “Feature Extraction and Sensor Fusion for Ultrasonic Structural Health Monitoring Under Changing Environmental Conditions,” IEEE Sensors Journal, Vol. 9, No. 11, pp. 1462–1471.
Lynch, J.P., C.R. Farrar, and J.E. Michaels, 2016, “Structural Health Monitoring: Technological Advances to Practical Implementations [Scanning the Issue],” Proceedings of the IEEE, Vol. 104, No. 8, pp. 1508–1512.
Memmolo, V., F. Ricci, L. Maio, N.D. Boffa, and E. Monaco, 2016, “Model Assisted Probability of Detection for a Guided Waves Based SHM Technique,” Health Monitoring of Structural and Biological Systems 2016, doi: 10.1117/12.2219306.
Moriot, J., N. Quaegebeur, A. Le Duff, and P. Masson, 2018, “A Model-Based Approach for Statistical Assessment of Detection and Localization Performance of Guided Wave–Based Imaging Techniques,” Structural Health Monitoring, Vol. 17, No. 6, pp. 1460–1472.
Peherstorfer, B., K. Willcox, and M. Gunzburger, 2018, “Survey of Multifidelity Methods in Uncertainty Propagation, Inference, and Optimization,” SIAM Review, Vol. 60, No. 3, pp. 550–591.
Ubertini, F., S. Laflamme, E. Chatzi, B. Glisic, and F. Magalhães, 2017, “Dense Sensor Networks for Mesoscale SHM: Innovations in Sensing Technologies and Signal Processing,” Measurement Science and Technology, Vol. 28, No. 4, doi: 10.1088/1361-6501/aa5c44.
Yan, J., X. Du, A. Downey, A. Cancelli, S. Laflamme, L. Leifsson, A. Chen, and F. Ubertini, 2018, “Surrogate Model for Condition Assessment of Structures Using a Dense Sensor Network,” Proceedings of SPIE: Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2018, Vol. 10598, doi: 10.1117/12.2296711.
Yan, J., X. Du, S. Laflamme, L. Leifsson, C. Hu, and A. Chen, 2019, “Model-Assisted Validation of A Strain-Based Dense Sensor Network,” Proceedings of SPIE: Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2019, Vol. 10970, doi: 10.1117/12.2515232.
46 Page Views
0 PDF Downloads
0 Facebook Shares