Damage location estimation is a critical aspect of condition-based maintenance systems, particularly in the context of electromechanical impedance monitoring. Various approaches have been developed to estimate damage location, yet they often need more capability to assess the reliability of data collected from sensor grids. In this paper, we introduce a novel method based on Sequential Gaussian Simulation (SGS) to pinpoint damage locations on aluminum plates and create maps that illustrate the spatial uncertainty associated with damage index values throughout the structure. Our proposed approach builds upon the SGS method and encompasses the assessment of four different sensor grid configurations to investigate how sensor spacing affects spatial uncertainty. The findings demonstrate the technique’s effectiveness in accurately predicting damage positions. Moreover, by leveraging the uncertainty information generated, we can identify specific areas necessitating careful attention, thus offering valuable insights for optimizing sensor grid design.
L’estimation de la localisation des dommages est un aspect critique des systèmes de maintenance basés sur l’état, en particulier dans le contexte de la surveillance de l’impédance électromécanique. Diverses approches ont été mises au point pour estimer la localisation des dommages, mais elles nécessitent souvent davantage de capacités pour évaluer la fiabilité des données collectées à partir des grilles de capteurs. Dans cet article, nous présentons une nouvelle méthode basée sur la simulation gaussienne séquentielle (SGS) pour localiser les dommages sur les plaques d’aluminium et créer des cartes qui illustrent l’incertitude spatiale associée aux valeurs de l’indice de dommage dans l’ensemble de la structure. L’approche proposée s’appuie sur la méthode SGS et englobe l’évaluation de quatre configurations différentes de la grille de capteurs afin d’étudier comment l’espacement des capteurs affecte l’incertitude spatiale. Les résultats démontrent l’efficacité de la technique pour prédire avec précision les positions des dommages. De plus, en exploitant les informations d’incertitude générées, nous pouvons identifier des zones spécifiques nécessitant une attention particulière, offrant ainsi des indications précieuses pour optimiser la conception de la grille de capteurs.
Revised:
Accepted:
Published online:
Mots-clés : Surveillance de la santé des structures, Méthode basée sur l’impédance électromécanique, Détection et localisation des dommages, Simulation gaussienne séquentielle
Paulo Elias Carneiro Pereira 1; Stanley Washington Ferreira de Rezende 2; José dos Reis Vieira de Moura Júnior 3; Roberto Mendes Finzi Neto 2
CC-BY 4.0
@article{CRMECA_2024__352_G1_19_0,
author = {Paulo Elias Carneiro Pereira and Stanley Washington Ferreira de Rezende and Jos\'e dos Reis Vieira de Moura J\'unior and Roberto Mendes Finzi Neto},
title = {SGS method applied to damage location and~uncertainty modeling for sensor grid {in~the~ISHM}},
journal = {Comptes Rendus. M\'ecanique},
pages = {19--37},
publisher = {Acad\'emie des sciences, Paris},
volume = {352},
year = {2024},
doi = {10.5802/crmeca.239},
language = {en},
}
TY - JOUR AU - Paulo Elias Carneiro Pereira AU - Stanley Washington Ferreira de Rezende AU - José dos Reis Vieira de Moura Júnior AU - Roberto Mendes Finzi Neto TI - SGS method applied to damage location and uncertainty modeling for sensor grid in the ISHM JO - Comptes Rendus. Mécanique PY - 2024 SP - 19 EP - 37 VL - 352 PB - Académie des sciences, Paris DO - 10.5802/crmeca.239 LA - en ID - CRMECA_2024__352_G1_19_0 ER -
%0 Journal Article %A Paulo Elias Carneiro Pereira %A Stanley Washington Ferreira de Rezende %A José dos Reis Vieira de Moura Júnior %A Roberto Mendes Finzi Neto %T SGS method applied to damage location and uncertainty modeling for sensor grid in the ISHM %J Comptes Rendus. Mécanique %D 2024 %P 19-37 %V 352 %I Académie des sciences, Paris %R 10.5802/crmeca.239 %G en %F CRMECA_2024__352_G1_19_0
Paulo Elias Carneiro Pereira; Stanley Washington Ferreira de Rezende; José dos Reis Vieira de Moura Júnior; Roberto Mendes Finzi Neto. SGS method applied to damage location and uncertainty modeling for sensor grid in the ISHM. Comptes Rendus. Mécanique, Volume 352 (2024), pp. 19-37. doi : 10.5802/crmeca.239. https://comptes-rendus.academie-sciences.fr/mecanique/articles/10.5802/crmeca.239/
[1] Structural Health Monitoring with Piezoelectric Wafer Active Sensors, Academic Press, Waltham, 2014 | DOI
[2] Electromechanical impedance modeling, Encyclopedia of Structural Health Monitoring (C. Boller; F.-K. Chang; Y. Fujino, eds.), John Wiley & Sons, Chichester, 2009, p. 5 | DOI
[3] Non-parametric inference applied to damage detection in the electromechanical impedance-based health monitoring, Int. J. Adv. Eng. Res. Sci., Volume 7 (2020) no. 9, pp. 73-79 | DOI
[4] Electromechanical impedance – based structural health monitoring instrumentation system applied to aircraft structures and employing a multiplexed sensor array, J. Aerosp. Technol. Manag., Volume 7 (2015) no. 3, pp. 294-306 | DOI
[5] A low-cost electromechanical impedance-based SHM architecture for multiplexed piezoceramic actuators, Struct. Health Monit., Volume 10 (2011) no. 4, pp. 391-402 | DOI
[6] ISHM for fault condition detection in rotating machines with deep learning models, J. Braz. Soc. Mech. Sci. Eng., Volume 45 (2023), 212 | DOI
[7] Impedance-based fault detection methodology for rotating machines, Struct. Health Monit., Volume 14 (2015) no. 3, pp. 228-240 | DOI
[8] Damage localization for CFRP-debonding defects using piezoelectric SHM techniques, Res. Nondestruct. Eval., Volume 23 (2012) no. 4, pp. 183-196 | DOI
[9] Damage localization map using electromechanical impedance spectrums and inverse distance weighting interpolation: Experimental validation on thin composite structures, Struct. Health Monit., Volume 12 (2013) no. 4, pp. 311-324 | DOI
[10] New imaging algorithm for material damage localisation based on impedance measurements under noise influence, Measurement, Volume 163 (2020), 107953 | DOI
[11] Electromechanical impedance-based damage localization with novel signatures extraction methodology and modified probability-weighted algorithm, Mech. Syst. Signal Process., Volume 146 (2021), 107001 | DOI
[12] Electromechanical impedance based debond localisation in a composite sandwich structure, J. Intell. Mater. Syst. Struct., Volume 33 (2022) no. 12, pp. 1487-1496 | DOI
[13] Monitoramento de integridade estrutural baseado em impedância eletromecânica utilizando o método de krigagem ordinária, Holos, Volume 36 (2020) no. 2, pp. 1-16 | DOI
[14] Indicator kriging for damage position prediction by the use of electromechanical impedance-based structural health monitoring, C. R. Méc., Volume 349 (2021) no. 2, pp. 225-240 | DOI
[15] Damage localization using electromechanical impedance technique based on inverse implementation, Struct. Health Monit., Volume 22 (2023) no. 5, pp. 3373-3384 | DOI
[16] Synchronous detection of bolts looseness position and degree based on fusing electro-mechanical impedance, Mech. Syst. Signal Process., Volume 174 (2022), 109068 | DOI
[17] Damage identification in plate structures using sparse regularization based electromechanical impedance technique, Sensors, Volume 20 (2020) no. 24, 7069 | DOI
[18] Grid-based analysis of air pollution data, Ecol. Model., Volume 194 (2006) no. 1, pp. 274-286 | DOI
[19] Electromechanical impedance method, Advanced Structural Damage Dectection: From Theory to Engineering Applications (T. Stepinski; T. Uhl; W. Staszewski, eds.), John Wiley & Sons, Chichester, 2013, pp. 141-176 | DOI
[20] Overview of piezoelectric impedance-based health monitoring and path forward, Shock Vibr. Dig., Volume 35 (2003) no. 6, pp. 451-463 | DOI
[21] Coupled electro-mechanical analysis of adaptive material systems-determination of the actuator power consumption and system energy transfer, J. Intell. Mater. Syst. Struct., Volume 8 (1997) no. 4, pp. 335-343 | DOI
[22] Electro-mechanical impedance technique, Smart Materials in Structural Health Monitoring, Control and Biomechanics (C.-K. Soh; Y. Yang; S. Bhalla, eds.), Springer, Berlin, 2012, pp. 17-51 | DOI
[23] Truss structure integrity identification using PZT sensor-actuator, J. Intell. Mater. Syst. Struct., Volume 6 (1995) no. 1, pp. 134-139 | DOI
[24] Recent advancements in the electromechanical (E/M) impedance method for structural health monitoring and NDE, Smart Structures and Materials 1998: Smart Structures and Integrated Systems (M. E. Regelbrugge, ed.), Volume 3329, International Society for Optics and Photonics, SPIE, San Diego, 1998, pp. 536-547 | DOI
[25] Principles of geostatistics, Econ. Geol., Volume 58 (1963) no. 8, pp. 1246-1266 | DOI
[26] Kriging, or polynomial interpolation procedures? A contribution to polemics in mathematical geology, Trans. Canad. Inst. Min. Metal., Volume 70 (1967), pp. 240-244
[27] The Theory of Regionalized Variables and its Applications, Les cahiers du Centre de Morphologie Mathématique de Fontainebleau, 05, École Nationale Supérieure des Mines de Paris, Paris, France, 1971
[28] The intrinsic random functions and their applications, Adv. Appl. Probab., Volume 5 (1973) no. 3, pp. 439-468 | DOI | MR | Zbl
[29] Geological modeling by an indicator kriging approach applied to a limestone deposit in Indiara city – Goiás, REM - Int. Eng. J., Volume 70 (2017) no. 3, pp. 331-337 | DOI
[30] Application of covariance table for geostatistical modeling in the presence of an exhaustive secondary variable, J. Petroleum Sci. Eng., Volume 196 (2021), 108073 | DOI
[31] Geostatistical analysis of uranium concentrations in North-Western part of Ogun State, Nigeria, J. Environ. Radioact., Volume 237 (2021), 106706 | DOI
[32] Contributions from experimental geostatistical analyses for solving the cloud-cover problem in remote sensing data, Int. J. Appl. Earth Obs. Geoinf., Volume 118 (2023), 103236 | DOI
[33] Geostatistics: Modeling Spatial Uncertainty, Wiley Series in Probability and Statistics, John Wiley & Sons, Hoboken, 2012 | DOI
[34] Mining Geostatistics, Academic Press Limited, London, 1978
[35] Applied Mining Geology, Springer International Publishing AG, Cham, 2016 | DOI
[36] Applied Mineral Inventory Estimation, Cambridge University Press, Cambridge, 2002 | DOI
[37] Geostatistical Reservoir Modeling, Oxford University Press, New York, 2014
[38] Basic Linear Geostatistics, Springer, Heidelberg, 1998 | DOI
[39] An Introduction to Applied Geostatistics, Oxford University Press, New York, 1989
[40] Geoestatística: Conceitos e Aplicações, Oficina de Textos, São Paulo, 2013
[41] Mineral Resource Estimation, Springer Science+Business Media, Dordrecht, 2014 | DOI
[42] Geostatistical Simulation: Models and Algorithms, Springer-Verlag, Heidelberg, 2002 | DOI
[43] Pygeostat, 2021 https://pypi.org/project/pygeostat/ (version 1.1.1, Centre for Computational Geostatistics)
[44] GeostatsPy Python Package, 2021 https://pypi.org/project/geostatspy/ (version 0.0.26, Texas Center for Data Analytics and Geostatistics)
[45] Characterization and geostatistical modeling of reservoirs in ‘Falad’ field, Niger Delta, Nigeria, J. Pet. Explor. Prod. Technol., Volume 12 (2022), pp. 1353-1369 | DOI
[46] 3D static reservoir modelling to evaluate petroleum potential of Goru C-Interval sands in Sawan Gas Field, Pakistan, Episodes, Volume 46 (2023) no. 1, pp. 1-18 | DOI
[47] Depiction of different alteration zones using fractal and simulation algorithm in Pulang Porphyry Copper Deposit, Southwest China, Nat. Resour. Res., Volume 31 (2022), pp. 1943-1961 | DOI
[48] Evaluating and modeling the groundwater in Hamedan plain aquifer, Iran, using the linear geostatistical estimation, sequential Gaussian simulation, and turning band simulation approaches, Model. Earth Syst. Environ., Volume 8 (2022), pp. 3555-3576 | DOI
[49] Health risk assessment of soil trace elements using the Sequential Gaussian Simulation approach, Environ. Sci. Pollut. Res., Volume 29 (2022), pp. 72683-72698 | DOI
[50] Prediction of the concentration of cadmium in agricultural soil in the Czech Republic using legacy data, preferential sampling, Sentinel-2, Landsat-8, and ensemble models, J. Environ. Manage., Volume 330 (2023), 117194 | DOI
Cited by Sources:
Comments - Policy