{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T16:19:52Z","timestamp":1761581992330,"version":"build-2065373602"},"reference-count":28,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2020,7,28]],"date-time":"2020-07-28T00:00:00Z","timestamp":1595894400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004281","name":"Narodowe Centrum Nauki","doi-asserted-by":"publisher","award":["2016\/22\/E\/ST8\/00068"],"award-info":[{"award-number":["2016\/22\/E\/ST8\/00068"]}],"id":[{"id":"10.13039\/501100004281","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>There is continuing research in the area of structural health monitoring (SHM) as it may allow a reduction in maintenance costs as well as lifetime extension. The search for a low-cost health monitoring system that is able to detect small levels of damage is still on-going. The present study is one more step in this direction. This paper describes a data fusion technique by combining the information for robust damage detection using the electromechanical impedance (EMI) method. The EMI method is commonly used for damage detection due to its sensitivity to low levels of damage. In this paper, the information of resistance (R) and conductance (G) is studied in a selected frequency band and a novel data fusion approach is proposed. A novel fused parameter (F) is developed by combining the information from G and R. The difference in the new metric under different damage conditions is then quantified using established indices such as the root mean square deviation (RMSD) index, mean absolute percentage deviation (MAPD), and root mean square deviation using k-th state as the reference (RMSDk). The paper presents an application of the new metric for detection of damage in three structures, namely, a thin aluminum (Al) plate with increasing damage severity (simulated with a drilled hole of increasing size), a glass fiber reinforced polymer (GFRP) composite beam with increasing delamination and another GFRP plate with impact-induced damage scenarios. Based on the experimental results, it is apparent that the variable F increases the robustness of the damage detection as compared to the quantities R and G.<\/jats:p>","DOI":"10.3390\/s20154204","type":"journal-article","created":{"date-parts":[[2020,7,29]],"date-time":"2020-07-29T07:31:45Z","timestamp":1596007905000},"page":"4204","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["A Variable Data Fusion Approach for Electromechanical Impedance-Based Damage Detection"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6094-8942","authenticated-orcid":false,"given":"Shishir Kumar","family":"Singh","sequence":"first","affiliation":[{"name":"Institute of Fluid Flow Machinery, Polish Academy of Sciences, 80-231 Gdansk, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5499-2565","authenticated-orcid":false,"given":"Rohan","family":"Soman","sequence":"additional","affiliation":[{"name":"Institute of Fluid Flow Machinery, Polish Academy of Sciences, 80-231 Gdansk, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1414-2969","authenticated-orcid":false,"given":"Tomasz","family":"Wandowski","sequence":"additional","affiliation":[{"name":"Institute of Fluid Flow Machinery, Polish Academy of Sciences, 80-231 Gdansk, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0478-2081","authenticated-orcid":false,"given":"Pawel","family":"Malinowski","sequence":"additional","affiliation":[{"name":"Institute of Fluid Flow Machinery, Polish Academy of Sciences, 80-231 Gdansk, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,7,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Qing, X., Li, W., Wang, Y., and Sun, H. (2019). Piezoelectric transducer-based structural health monitoring for aircraft applications. Sensors, 19.","DOI":"10.3390\/s19030545"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Perera, R., Torres, L., Ruiz, A., Barris, C., and Baena, M. (2019). An EMI-based clustering for structural health monitoring of NSM FRP strengthening systems. Sensors, 19.","DOI":"10.3390\/s19173775"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Ezzat, A.A., Tang, J., and Ding, Y. A model-based calibration approach for structural fault diagnosis using piezoelectric impedance measurements and a finite element model. Struct. Health Monit., 2020.","DOI":"10.1177\/1475921719901168"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1177\/1045389X09352816","article-title":"Application of electromechanical impedance technique for engineering structures: Review and future issues","volume":"21","author":"Annamdas","year":"2010","journal-title":"J. Intell. Mater. Syst. Struct."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"472","DOI":"10.1016\/j.conbuildmat.2018.01.039","article-title":"Mechanical impedance based embedded piezoelectric transducer for reinforced concrete structural impact damage detection: A comparative study","volume":"165","author":"Ai","year":"2018","journal-title":"Constr. Build. Mater."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"104004","DOI":"10.1088\/1361-665X\/aa7ef3","article-title":"Crack detection in pipelines using multiple electromechanical impedance sensors","volume":"26","author":"Zuo","year":"2017","journal-title":"Smart Mater. Struct."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Zhu, J., Wang, Y., and Qing, X. (2019). Modified Electromechanical Impedance-based Disbond Monitoring for Honeycomb Sandwich Composite Structure. Compos. Struct., 217.","DOI":"10.1016\/j.compstruct.2019.03.033"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"107001","DOI":"10.1016\/j.ymssp.2020.107001","article-title":"Electromechanical impedance-based damage localization with novel signatures extraction methodology and modified probability-weighted algorithm","volume":"146","author":"Zhu","year":"2021","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"045009","DOI":"10.1088\/0964-1726\/19\/4\/045009","article-title":"Hierarchical ensemble-based data fusion for structural health monitoring","volume":"19","author":"Zhao","year":"2010","journal-title":"Smart Mater. Struct."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1619","DOI":"10.1109\/TPAMI.2006.211","article-title":"Rotation forest: A new classifier ensemble method","volume":"28","author":"Rodriguez","year":"2006","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"552","DOI":"10.1177\/1475921718798769","article-title":"Data fusion approaches for structural health monitoring and system identification: Past, present, and future","volume":"19","author":"Wu","year":"2020","journal-title":"Struct. Health Monit."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"4392","DOI":"10.1109\/TIE.2017.2764844","article-title":"NB-CNN: Deep learning-based crack detection using convolutional neural network and Na\u00efve Bayes data fusion","volume":"65","author":"Chen","year":"2017","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_13","first-page":"480","article-title":"Statistical process monitoring: Basics and beyond","volume":"17","year":"2003","journal-title":"J. Chemom. A J. Chemom. Soc."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1177\/1475921710388972","article-title":"Q-statistic and T2-statistic PCA-based measures for damage assessment in structures","volume":"10","author":"Mujica","year":"2011","journal-title":"Struct. Health Monit."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1303","DOI":"10.1002\/stc.1540","article-title":"Damage classification in structural health monitoring using principal component analysis and self-organizing maps","volume":"20","author":"Tibaduiza","year":"2013","journal-title":"Struct. Control Health Monit."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1177\/1045389X07077400","article-title":"Electro-mechanical impedance-based wireless structural health monitoring using PCA-data compression and k-means clustering algorithms","volume":"19","author":"Park","year":"2008","journal-title":"J. Intell. Mater. Syst. Struct."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1109\/5.554206","article-title":"Sensor fusion potential exploitation-innovative architectures and illustrative applications","volume":"85","author":"Dasarathy","year":"1997","journal-title":"Proc. IEEE"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1109\/JSEN.2002.1000251","article-title":"Multisensor fusion and integration: Approaches, applications, and future research directions","volume":"2","author":"Luo","year":"2002","journal-title":"IEEE Sens. J."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Castanedo, F. (2013). A review of data fusion techniques. Sci. World J., 2013.","DOI":"10.1155\/2013\/704504"},{"key":"ref_20","first-page":"301","article-title":"Advances and challenges in impedance-based structural health monitoring","volume":"4","author":"Huynh","year":"2017","journal-title":"Struct. Monit. Maint"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"045036","DOI":"10.1088\/1361-665X\/aa5f40","article-title":"Load monitoring using a calibrated piezo diaphragm based impedance strain sensor and wireless sensor network in real time","volume":"26","author":"Annamdas","year":"2017","journal-title":"Smart Mater. Struct."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Perera, R., P\u00e9rez, A., Garc\u00eda-Di\u00e9guez, M., and Zapico-Valle, J.L. (2017). Active wireless system for structural health monitoring applications. Sensors, 17.","DOI":"10.3390\/s17122880"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1208","DOI":"10.3390\/s140101208","article-title":"An experimental study on the effect of temperature on piezoelectric sensors for impedance-based structural health monitoring","volume":"14","author":"Baptista","year":"2014","journal-title":"Sensors"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2383","DOI":"10.1016\/j.compstruct.2012.02.022","article-title":"Resonant frequency range utilized electro-mechanical impedance method for damage detection performance enhancement on composite structures","volume":"94","author":"Na","year":"2012","journal-title":"Compos. Struct."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"673","DOI":"10.1080\/15732479.2017.1350984","article-title":"Numerical evaluation of multi-metric data fusion based structural health monitoring of long span bridge structures","volume":"14","author":"Soman","year":"2018","journal-title":"Struct. Infrastruct. Eng."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1177\/1475921705049752","article-title":"Damage detection in thin plates and aerospace structures with the electro-mechanical impedance method","volume":"4","author":"Giurgiutiu","year":"2005","journal-title":"Struct. Health Monit."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Na, S., and Baek, J. (2018). A review of the piezoelectric electromechanical impedance based structural health monitoring technique for engineering structures. Sensors, 18.","DOI":"10.3390\/s18051307"},{"key":"ref_28","unstructured":"Skarbek, L., Wandowski, T., Opoka, S., Malinowski, P., and Ostachowicz, W. (2012, January 3\u20136). Electromechanical impedance technique and scanning vibrometry for structure characterization. Proceedings of the 6th European Workshop on Structural Health Monitoring, Dresden, Germany."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/15\/4204\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:52:25Z","timestamp":1760176345000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/15\/4204"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,28]]},"references-count":28,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2020,8]]}},"alternative-id":["s20154204"],"URL":"https:\/\/doi.org\/10.3390\/s20154204","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2020,7,28]]}}}