{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T18:17:31Z","timestamp":1775067451038,"version":"3.50.1"},"reference-count":40,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2020,3,26]],"date-time":"2020-03-26T00:00:00Z","timestamp":1585180800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003329","name":"Ministerio de Econom\u00eda y Competitividad","doi-asserted-by":"publisher","award":["DPI2017-82930-C2-1-R"],"award-info":[{"award-number":["DPI2017-82930-C2-1-R"]}],"id":[{"id":"10.13039\/501100003329","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002809","name":"Generalitat de Catalunya","doi-asserted-by":"publisher","award":["2017 SGR 388"],"award-info":[{"award-number":["2017 SGR 388"]}],"id":[{"id":"10.13039\/501100002809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Structural health monitoring for offshore wind turbines is imperative. Offshore wind energy is progressively attained at greater water depths, beyond 30 m, where jacket foundations are presently the best solution to cope with the harsh environment (extreme sites with poor soil conditions). Structural integrity is of key importance in these underwater structures. In this work, a methodology for the diagnosis of structural damage in jacket-type foundations is stated. The method is based on the criterion that any damage or structural change produces variations in the vibrational response of the structure. Most studies in this area are, primarily, focused on the case of measurable input excitation and vibration response signals. Nevertheless, in this paper it is assumed that the only available excitation, the wind, is not measurable. Therefore, using vibration-response-only accelerometer information, a data-driven approach is developed following the next steps: (i) the wind is simulated as a Gaussian white noise and the accelerometer data are collected; (ii) the data are pre-processed using group-reshape and column-scaling; (iii) principal component analysis is used for both linear dimensionality reduction and feature extraction; finally, (iv) two different machine-learning algorithms, k nearest neighbor (k-NN) and quadratic-kernel support vector machine (SVM), are tested as classifiers. The overall accuracy is estimated by 5-fold cross-validation. The proposed approach is experimentally validated in a laboratory small-scale structure. The results manifest the reliability of the stated fault diagnosis method being the best performance given by the SVM classifier.<\/jats:p>","DOI":"10.3390\/s20071835","type":"journal-article","created":{"date-parts":[[2020,4,1]],"date-time":"2020-04-01T03:44:13Z","timestamp":1585712653000},"page":"1835","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["Structural Health Monitoring for Jacket-Type Offshore Wind Turbines: Experimental Proof of Concept"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4964-6948","authenticated-orcid":false,"given":"Yolanda","family":"Vidal","sequence":"first","affiliation":[{"name":"Control, Modeling, Identification and Applications (CoDAlab), Department of Mathematics, Escola d\u2019Enginyeria de Barcelona Est (EEBE), Universitat Polit\u00e8cnica de Catalunya (UPC), Campus Diagonal-Bes\u00f2s (CDB), Eduard Maristany, 16, 08019 Barcelona, Spain"}]},{"given":"Gabriela","family":"Aquino","sequence":"additional","affiliation":[{"name":"Facultad de Ciencias de la Electr\u00f3nica (FCE), Benem\u00e9rita Universidad Aut\u00f3noma de Puebla (BUAP), Av. San Claudio y 18 Sur, Ciudad Universitaria, Edificio 1FCE6\/202, 72570 Puebla, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8958-6789","authenticated-orcid":false,"given":"Francesc","family":"Pozo","sequence":"additional","affiliation":[{"name":"Control, Modeling, Identification and Applications (CoDAlab), Department of Mathematics, Escola d\u2019Enginyeria de Barcelona Est (EEBE), Universitat Polit\u00e8cnica de Catalunya (UPC), Campus Diagonal-Bes\u00f2s (CDB), Eduard Maristany, 16, 08019 Barcelona, Spain"}]},{"given":"Jos\u00e9 Eligio Mois\u00e9s","family":"Guti\u00e9rrez-Arias","sequence":"additional","affiliation":[{"name":"Facultad de Ciencias de la Electr\u00f3nica (FCE), Benem\u00e9rita Universidad Aut\u00f3noma de Puebla (BUAP), Av. San Claudio y 18 Sur, Ciudad Universitaria, Edificio 1FCE6\/202, 72570 Puebla, Mexico"}]}],"member":"1968","published-online":{"date-parts":[[2020,3,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Klijnstra, J., Zhang, X., van der Putten, S., and R\u00f6ckmann, C. (2017). Aquaculture Perspective of Multi-Use Sites in the Open Ocean, Springer. Technical Risks of Offshore Structures.","DOI":"10.1007\/978-3-319-51159-7_5"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Fritzen, C.P. (2006). Structural Health Monitoring, Wiley. Vibration-Based Techniques for Structural Health Monitoring.","DOI":"10.1002\/9780470612071.ch2"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1098\/rsta.2006.1929","article-title":"Time-series methods for fault detection and identification in vibrating structures","volume":"365","author":"Fassois","year":"2006","journal-title":"Philos. Trans. R. Soc. A"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"585","DOI":"10.1007\/s11831-015-9145-0","article-title":"The vibration monitoring methods and signal processing techniques for structural health monitoring: a review","volume":"23","author":"Goyal","year":"2016","journal-title":"Arch. Comput. Meth. Eng."},{"key":"ref_5","unstructured":"Vamvoudakis-Stefanou, K.J., Sakellariou, J.S., and Fassois, S.D. (2014, January 8\u201311). Output-only statistical time series methods for structural health monitoring: A comparative study. Proceedings of the 7th European Workshop on Structural Health Monitoring, Nantes, France."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"466","DOI":"10.1016\/j.rser.2014.12.005","article-title":"The structure healthy condition monitoring and fault diagnosis methods in wind turbines: A review","volume":"44","author":"Liu","year":"2015","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.rser.2016.05.085","article-title":"Structural health monitoring of offshore wind turbines: A review through the Statistical Pattern Recognition Paradigm","volume":"64","author":"Kolios","year":"2016","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Lian, J., Cai, O., Dong, X., Jiang, Q., and Zhao, Y. (2019). Health monitoring and safety evaluation of the offshore wind turbine structure: a review and discussion of future development. Sustainability, 11.","DOI":"10.3390\/su11020494"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.marstruc.2016.10.006","article-title":"An application of Structural Health Monitoring system based on FBG sensors to offshore wind turbine support structure model","volume":"51","author":"Mieloszyk","year":"2017","journal-title":"Mar. Struct."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Fritzen, C.P., Kraemer, P., and Klinkov, M. (2011). Structural Dynamics, Springer. An Integrated SHM Approach for Offshore Wind Energy Plants.","DOI":"10.1007\/978-1-4419-9834-7_63"},{"key":"ref_11","unstructured":"Schr\u00f6der, K., Gebhardt, C., and Rolfes, R. (2016, January 5\u20138). Damage Localization at Wind Turbine Support Structures Using Sequential Quadratic Programming for Model Updating. Proceedings of the 8th European Workshop on Structural Health Monitoring, Bilbao, Spain."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1177\/1475921715586624","article-title":"Foundation structural health monitoring of an offshore wind turbine\u2014A full-scale case study","volume":"15","author":"Weijtjens","year":"2016","journal-title":"Struct. Health Monit."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/j.marstruc.2010.01.005","article-title":"Damage detection in offshore structures using neural networks","volume":"23","author":"Elshafey","year":"2010","journal-title":"Mar. Struct."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"398","DOI":"10.1016\/j.apor.2011.05.001","article-title":"Developing a robust SHM method for offshore jacket platform using model updating and fuzzy logic system","volume":"33","author":"Mojtahedi","year":"2011","journal-title":"Appl. Ocean Res."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1490","DOI":"10.1016\/j.renene.2017.07.013","article-title":"Performance monitoring of a wind turbine using extreme function theory","volume":"113","author":"Papatheou","year":"2017","journal-title":"Renew. Energy"},{"key":"ref_16","unstructured":"Zugasti Uriguen, E. (2014). Design and Validation of a Methodology for Wind Energy Structures Health Monitoring. [Ph.D. Thesis, Universitat Polit\u00e8cnica de Catalunya]."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Pozo, F., and Vidal, Y. (2016). Wind turbine fault detection through principal component analysis and statistical hypothesis testing. Energies, 9.","DOI":"10.3390\/en9010003"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Ziegler, L., and Muskulus, M. (2016, January 18\u201324). Comparing a fracture mechanics model to the SN-curve approach for jacket-supported offshore wind turbines: Challenges and opportunities for lifetime prediction. Proceedings of the ASME 2016 35th International Conference on Ocean, Offshore and Arctic Engineering, Busan, Korea.","DOI":"10.1115\/OMAE2016-54915"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Pozo, F., Vidal, Y., and Serrahima, J. (2016). On real-time fault detection in wind turbines: Sensor selection algorithm and detection time reduction analysis. Energies, 9.","DOI":"10.3390\/en9070520"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Anaissi, A., Makki Alamdari, M., Rakotoarivelo, T., and Khoa, N. (2018). A tensor-based structural damage identification and severity assessment. Sensors, 18.","DOI":"10.3390\/s18010111"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1002\/(SICI)1099-128X(199905\/08)13:3\/4<397::AID-CEM559>3.0.CO;2-I","article-title":"Comparing alternative approaches for multivariate statistical analysis of batch process data","volume":"13","author":"Westerhuis","year":"1999","journal-title":"J. Chemom."},{"key":"ref_22","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_23","doi-asserted-by":"crossref","unstructured":"Pozo, F., Vidal, Y., and Salgado, \u00d3. (2018). Wind turbine condition monitoring strategy through multiway PCA and multivariate inference. Energies, 11.","DOI":"10.3390\/en11040749"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1627","DOI":"10.1109\/TSTE.2018.2801625","article-title":"Wind turbine fault detection and identification through PCA-based optimal variable selection","volume":"9","author":"Wang","year":"2018","journal-title":"IEEE Trans. Sustain. Energy"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"20150202","DOI":"10.1098\/rsta.2015.0202","article-title":"Principal component analysis: A review and recent developments","volume":"374","author":"Jolliffe","year":"2016","journal-title":"Philos. Trans. R. Soc. A"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1261","DOI":"10.1109\/TII.2016.2532321","article-title":"Wind turbine modeling with data-driven methods and radially uniform designs","volume":"12","author":"Tan","year":"2016","journal-title":"IEEE Trans. Ind. Informatics"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Vitola, J., Pozo, F., Tibaduiza, D.A., and Anaya, M. (2017). A sensor data fusion system based on k-nearest neighbor pattern classification for structural health monitoring applications. Sensors, 17.","DOI":"10.3390\/s17020417"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1023\/B:STCO.0000035301.49549.88","article-title":"A tutorial on support vector regression","volume":"14","author":"Smola","year":"2004","journal-title":"Stat. Comput."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Vidal, Y., Pozo, F., and Tutiv\u00e9n, C. (2018). Wind turbine multi-fault detection and classification based on SCADA data. Energies, 11.","DOI":"10.3390\/en11113018"},{"key":"ref_30","unstructured":"Theodoridis, S., and Koutroumbas, K. (2009). Pattern Recognition, Elsevier."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2758","DOI":"10.1109\/78.650102","article-title":"Comparing support vector machines with Gaussian kernels to radial basis function classifiers","volume":"45","author":"Scholkopf","year":"1997","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_32","first-page":"113","article-title":"Reducing multiclass to binary: A unifying approach for margin classifiers","volume":"1","author":"Allwein","year":"2000","journal-title":"J. Mach. Learn. Res."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.ymssp.2017.12.035","article-title":"Wind turbine fault detection and classification by means of image texture analysis","volume":"107","author":"Ruiz","year":"2018","journal-title":"Mech. Syst. Sig. Process."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1","DOI":"10.5121\/ijdkp.2015.5201","article-title":"A review on evaluation metrics for data classification evaluations","volume":"5","author":"Hossin","year":"2015","journal-title":"IJDKP"},{"key":"ref_35","unstructured":"Kr\u00fcger, F. (2016). Activity, Context, and Plan Recognition with Computational Causal Behaviour Models. [Ph.D. Thesis, University of Rostock]."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Hameed, N., Hameed, F., Shabut, A., Khan, S., Cirstea, S., and Hossain, A. (2019). An Intelligent Computer-Aided Scheme for Classifying Multiple Skin Lesions. Computers, 8.","DOI":"10.3390\/computers8030062"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1632","DOI":"10.1109\/JPROC.2016.2566602","article-title":"Three-tier modular structural health monitoring framework using environmental and operational condition clustering for data normalization: Validation on an operational wind turbine system","volume":"104","author":"Rolfes","year":"2016","journal-title":"Proc. IEEE"},{"key":"ref_38","unstructured":"Kraemer, P., Friedmann, H., Ebert, C., Mahowald, J., and W\u00f6lfel, B. (2016, January 5\u20138). Experimental validation of stochastic subspace algorithms for structural health monitoring of offshore wind turbine towers and foundations. Proceedings of the 8th European Workshop On Structural Health Monitoring, Bilbao, Spain."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"95","DOI":"10.4028\/www.scientific.net\/AST.83.95","article-title":"Vibration-based damage detection under changing environmental and operational conditions","volume":"83","author":"Fritzen","year":"2013","journal-title":"Adv. Sci. Technol. Water Resour."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Ostachowicz, W., and G\u00fcemes, A. (2013). New Trends in Structural Health Monitoring, Springer Science & Business Media.","DOI":"10.1007\/978-3-7091-1390-5"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/7\/1835\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:11:50Z","timestamp":1760173910000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/7\/1835"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,3,26]]},"references-count":40,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2020,4]]}},"alternative-id":["s20071835"],"URL":"https:\/\/doi.org\/10.3390\/s20071835","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,3,26]]}}}