{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,12]],"date-time":"2026-07-12T02:49:00Z","timestamp":1783824540173,"version":"3.55.0"},"reference-count":61,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2022,1,13]],"date-time":"2022-01-13T00:00:00Z","timestamp":1642032000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Spanish Ministry of Economy and Competitiveness","award":["RTC-2017-6312-7"],"award-info":[{"award-number":["RTC-2017-6312-7"]}]},{"DOI":"10.13039\/501100003086","name":"Basque Government","doi-asserted-by":"publisher","award":["KK-2020\/00044"],"award-info":[{"award-number":["KK-2020\/00044"]}],"id":[{"id":"10.13039\/501100003086","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The maintenance of industrial equipment extends its useful life, improves its efficiency, reduces the number of failures, and increases the safety of its use. This study proposes a methodology to develop a predictive maintenance tool based on infrared thermographic measures capable of anticipating failures in industrial equipment. The thermal response of selected equipment in normal operation and in controlled induced anomalous operation was analyzed. The characterization of these situations enabled the development of a machine learning system capable of predicting malfunctions. Different options within the available conventional machine learning techniques were analyzed, assessed, and finally selected for electronic equipment maintenance activities. This study provides advances towards the robust application of machine learning combined with infrared thermography and augmented reality for maintenance applications of industrial equipment. The predictive maintenance system finally selected enables automatic quick hand-held thermal inspections using 3D object detection and a pose estimation algorithm, making predictions with an accuracy of 94% at an inference time of 0.006 s.<\/jats:p>","DOI":"10.3390\/s22020613","type":"journal-article","created":{"date-parts":[[2022,1,14]],"date-time":"2022-01-14T03:14:56Z","timestamp":1642130096000},"page":"613","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":40,"title":["Towards the Automation of Infrared Thermography Inspections for Industrial Maintenance Applications"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8508-057X","authenticated-orcid":false,"given":"Pablo","family":"Venegas","sequence":"first","affiliation":[{"name":"Aeronautical Technologies Centre (CTA), 01510 Mi\u00f1ano, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6062-2061","authenticated-orcid":false,"given":"Eugenio","family":"Ivorra","sequence":"additional","affiliation":[{"name":"Institute for Research and Innovation in Bioengineering, Polytechnic University of Valencia, 46022 Valencia, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4467-7094","authenticated-orcid":false,"given":"Mario","family":"Ortega","sequence":"additional","affiliation":[{"name":"Institute for Research and Innovation in Bioengineering, Polytechnic University of Valencia, 46022 Valencia, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Idurre","family":"S\u00e1ez de Oc\u00e1riz","sequence":"additional","affiliation":[{"name":"Aeronautical Technologies Centre (CTA), 01510 Mi\u00f1ano, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1007\/s13198-011-0045-x","article-title":"Maintenance optimization models and criteria","volume":"1","author":"Pintelon","year":"2010","journal-title":"Int. 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Manuf."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"12305","DOI":"10.3390\/s140712305","article-title":"Infrared thermography for temperature measurement and non-destructive testing","volume":"14","author":"Usamentiaga","year":"2014","journal-title":"Sensors"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.infrared.2017.12.015","article-title":"Localization of thermal anomalies in electrical equipment using Infrared Thermography and support vector machine","volume":"89","author":"Mansour","year":"2018","journal-title":"Infrared Phys. Technol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1901","DOI":"10.1109\/TIA.2017.2655008","article-title":"Application of infrared thermography to failure detection in industrial induction motors: Case stories","volume":"53","year":"2017","journal-title":"IEEE Trans. Ind. Appl."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1016\/j.applthermaleng.2013.07.028","article-title":"Application of infrared thermography for predictive\/preventive maintenance of thermal defect in electrical equipment","volume":"61","author":"Huda","year":"2013","journal-title":"Appl. Therm. Eng."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Medeiros, C.C., do Nascimento, J.A., Rocha, A.N., Santos, E.M., de Lima Neta, R.M., Neto, J.M.G., and da Silva, A.A.P. (2018, January 12\u201316). Thermography in low voltage systems: Electrical panels and transformers. Proceedings of the 2018 Simposio Brasileiro de Sistemas Eletricos (SBSE), Niteroi, Brazil.","DOI":"10.1109\/SBSE.2018.8395832"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2947","DOI":"10.1007\/s12206-014-0701-6","article-title":"Fault diagnosis of rotating machine by thermography method on support vector machine","volume":"28","author":"Lim","year":"2014","journal-title":"J. Mech. Sci. Technol."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Rafailidis, D., and Manolopoulos, Y. (2018). The technological gap between virtual assistants and recommendation systems. arXiv.","DOI":"10.1145\/3326467.3326468"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"113074","DOI":"10.1016\/j.eswa.2019.113074","article-title":"Intelligent traffic control for autonomous vehicle systems based on machine learning","volume":"144","author":"Lee","year":"2020","journal-title":"Expert Syst. Appl."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Babanne, V., Mahajan, N.S., Sharma, R.L., and Gargate, P.P. (2019, January 12\u201314). Machine learning based smart surveillance system. Proceedings of the 2019 Third International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), Palladam, India.","DOI":"10.1109\/I-SMAC47947.2019.9032428"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"e01802","DOI":"10.1016\/j.heliyon.2019.e01802","article-title":"Machine learning for email spam filtering: Review, approaches and open research problems","volume":"5","author":"Dada","year":"2019","journal-title":"Heliyon"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s40436-015-0131-4","article-title":"A comprehensive survey of augmented reality assembly research","volume":"4","author":"Wang","year":"2016","journal-title":"Adv. Manuf."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1109\/TETC.2014.2368833","article-title":"Challenges, opportunities, and future trends of emerging techniques for augmented reality-based maintenance","volume":"2","author":"Lamberti","year":"2014","journal-title":"IEEE Trans. Emerg. Top. Comput."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/j.rcim.2017.06.002","article-title":"A systematic review of augmented reality applications in maintenance","volume":"49","author":"Palmarini","year":"2018","journal-title":"Robot. Comput.-Integr. Manuf."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"4096","DOI":"10.1016\/j.nucengdes.2010.08.023","article-title":"Heuristic guidelines and experimental evaluation of effective augmented-reality based instructions for maintenance in nuclear power plants","volume":"240","author":"Yim","year":"2010","journal-title":"Nucl. Eng. Des."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.infrared.2013.04.012","article-title":"Suitable features selection for monitoring thermal condition of electrical equipment using infrared thermography","volume":"61","author":"Huda","year":"2013","journal-title":"Infrared Phys. Technol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"643","DOI":"10.1016\/j.solener.2020.08.027","article-title":"A machine learning framework to identify the hotspot in photovoltaic module using infrared thermography","volume":"208","author":"Ali","year":"2020","journal-title":"Sol. Energy"},{"key":"ref_21","unstructured":"ASTM, A.E. (2005). Standard Guide for Examining Electrical and Mechanical Equipment with Infrared Thermography, ASTM International."},{"key":"ref_22","unstructured":"NFPA, N. (2006). 70B: Recommended Practice for Electrical Equipment Maintenance, National Fire Protection Association."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"751","DOI":"10.1109\/TPWRD.2009.2013375","article-title":"Intelligent thermographic diagnostic applied to surge arresters: A new approach","volume":"24","author":"Almeida","year":"2009","journal-title":"IEEE Trans. Power Deliv."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Shafi\u2019i, M.A., and Hamzah, N. (2010, January 23\u201324). Internal fault classification using artificial neural network. Proceedings of the 2010 4th International Power Engineering and Optimization Conference (PEOCO), Shah Alam, Malaysia.","DOI":"10.1109\/PEOCO.2010.5559176"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Kurukuru, V.B., Haque, A., Khan, M.A., and Tripathy, A.K. (2019, January 3\u20134). Fault classification for photovoltaic modules using thermography and machine learning techniques. Proceedings of the 2019 International Conference on Computer and Information Sciences (ICCIS), Sakaka, Saudi Arabia.","DOI":"10.1109\/ICCISci.2019.8716442"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Rahmani, A., Haddadnia, J., and Seryasat, O. (2010, January 1\u20133). Intelligent fault detection of electrical equipment in ground substations using thermo vision technique. Proceedings of the 2010 2nd International Conference on Mechanical and Electronics Engineering, Kyoto, Japan.","DOI":"10.1109\/ICMEE.2010.5558469"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"655","DOI":"10.1016\/j.procir.2015.12.069","article-title":"A cloud-based approach for maintenance of machine tools and equipment based on shop-floor monitoring","volume":"41","author":"Mourtzis","year":"2016","journal-title":"Procedia Cirp"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Ortega, M., Ivorra, E., Juan, A., Venegas, P., Mart\u00ednez, J., and Alca\u00f1iz, M. (2021). MANTRA: An Effective System Based on Augmented Reality and Infrared Thermography for Industrial Maintenance. Appl. Sci., 11.","DOI":"10.3390\/app11010385"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1016\/j.infrared.2012.03.007","article-title":"Medical applications of infrared thermography: A review","volume":"55","author":"Lahiri","year":"2012","journal-title":"Infrared Phys. Technol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.scienta.2012.01.022","article-title":"Determining the emissivity of the leaves of nine horticultural crops by means of infrared thermography","volume":"137","author":"Valera","year":"2012","journal-title":"Sci. Hortic."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Bhowmik, M.K., Saha, K., Majumder, S., Majumder, G., Saha, A., Sarma, A.N., Bhattacharjee, D., Basu, D.K., and Nasipuri, M. (2011). Thermal infrared face recognition\u2014A biometric identification technique for robust security system. Reviews, Refinements and New Ideas in Face Recognition, IntechOpen.","DOI":"10.5772\/18986"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1337","DOI":"10.1117\/1.1566969","article-title":"Reconstruction and enhancement of active thermographic image sequences","volume":"42","author":"Shepard","year":"2003","journal-title":"Opt. Eng."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Ibarra-Castanedo, C., Galmiche, F., Darabi, A., Pilla, M., Klein, M., Ziadi, A., Vallerand, S., Pelletier, J.F., and Maldague, X.P. (2003, January 21\u201325). Thermographic nondestructive evaluation: Overview of recent progress. Proceedings of the AeroSense 2003, Orlando, FL, USA.","DOI":"10.1117\/12.485699"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"661","DOI":"10.1016\/j.ndteint.2010.07.002","article-title":"Infrared thermography processing based on higher-order statistics","volume":"43","author":"Madruga","year":"2010","journal-title":"NDT E Int."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Venegas, P., Usamentiaga, R., Per\u00e1n, J., and S\u00e1ez de Oc\u00e1riz, I. (2021). Quaternion Processing Techniques for Color Synthesized NDT Thermography. Appl. Sci., 11.","DOI":"10.3390\/app11020790"},{"key":"ref_36","unstructured":"Shepard, S. (2004). System for Generating Thermographic Images Using Thermographic Signal Reconstruction. (6,751,342), US Patent."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1016\/S1350-4495(02)00138-X","article-title":"Advances in pulsed phase thermography","volume":"43","author":"Maldague","year":"2002","journal-title":"Infrared Phys. Technol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1016\/S0263-8223(02)00161-7","article-title":"Principal component thermography for flaw contrast enhancement and flaw depth characterisation in composite structures","volume":"58","author":"Rajic","year":"2002","journal-title":"Compos. Struct."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1126\/science.aaa8415","article-title":"Machine learning: Trends, perspectives, and prospects","volume":"349","author":"Jordan","year":"2015","journal-title":"Science"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Ketkar, N. (2017). Machine Learning Fundamentals. Deep Learning with Python, Springer.","DOI":"10.1007\/978-1-4842-2766-4"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Jiang, H. (2021). Machine Learning Fundamentals: A Concise Introduction, Cambridge University Press.","DOI":"10.1017\/9781108938051"},{"key":"ref_42","first-page":"17","article-title":"Ensemble decision tree classifier for breast cancer data","volume":"2","author":"Lavanya","year":"2012","journal-title":"Int. J. Inf. Technol. Converg. Serv."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1716","DOI":"10.1214\/15-AOS1321","article-title":"Consistency of random forests","volume":"43","author":"Scornet","year":"2015","journal-title":"Ann. Stat."},{"key":"ref_44","unstructured":"Cunningham, P., and Delany, S.J. (2020). k-Nearest neighbour classifiers: (With Python examples). arXiv."},{"key":"ref_45","unstructured":"Rachmawanto, E.H., Anarqi, G.R., and Sari, C.A. (2018, January 21\u201322). Handwriting Recognition Using Eccentricity and Metric Feature Extraction Based on K-Nearest Neighbors. Proceedings of the 2018 International Seminar on Application for Technology of Information and Communication, Semarang, Indonesia."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1016\/j.rse.2008.06.015","article-title":"Diagnostic tools for nearest neighbors techniques when used with satellite imagery","volume":"113","author":"McRoberts","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Zhang, Y. (2012, January 14\u201316). Support vector machine classification algorithm and its application. Proceedings of the International Conference on Information Computing and Applications, Chengde, China.","DOI":"10.1007\/978-3-642-34041-3_27"},{"key":"ref_48","first-page":"71","article-title":"Support vector machine applications in computational biology","volume":"14","author":"Noble","year":"2004","journal-title":"Kernel Methods Comput. Biol."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Nasien, D., Haron, H., and Yuhaniz, S.S. (2010, January 19\u201321). Support Vector Machine (SVM) for English handwritten character recognition. Proceedings of the 2010 Second International Conference on Computer Engineering and Applications, Bali, Indonesia.","DOI":"10.1109\/ICCEA.2010.56"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/S0167-7012(00)00201-3","article-title":"Artificial neural networks: Fundamentals, computing, design, and application","volume":"43","author":"Basheer","year":"2000","journal-title":"J. Microbiol. Methods"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Rao, Q., and Frtunikj, J. (2018, January 28). Deep learning for self-driving cars: Chances and challenges. Proceedings of the 1st International Workshop on Software Engineering for AI in Autonomous Systems, Gothenburg, Sweden.","DOI":"10.1145\/3194085.3194087"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Narasimhan, A., and Rao, K.P.A.V. (2021). CGEMs: A Metric Model for Automatic Code Generation using GPT-3. arXiv.","DOI":"10.1145\/3551349.3559548"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Desai, S., Raghavendra, E.V., Yegnanarayana, B., Black, A.W., and Prahallad, K. (2009, January 19\u201324). Voice conversion using artificial neural networks. Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing, Taipei, Taiwan.","DOI":"10.1109\/ICASSP.2009.4960478"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"R921","DOI":"10.1016\/j.cub.2014.08.026","article-title":"Neural networks and neuroscience-inspired computer vision","volume":"24","author":"Cox","year":"2014","journal-title":"Curr. Biol."},{"key":"ref_55","first-page":"120","article-title":"The openCV library","volume":"25","author":"Bradski","year":"2000","journal-title":"Dr. Dobb\u2019s J. Softw. Tools Prof. Program."},{"key":"ref_56","unstructured":"Bochkovskiy, A., Wang, C.Y., and Liao, H.Y.M. (2020). Yolov4: Optimal speed and accuracy of object detection. arXiv."},{"key":"ref_57","unstructured":"Zhou, Q.Y., Park, J., and Koltun, V. (2018). Open3D: A modern library for 3D data processing. arXiv."},{"key":"ref_58","unstructured":"Farber, R. (2011). CUDA Application Design and Development, Elsevier."},{"key":"ref_59","unstructured":"Hackeling, G. (2017). Mastering Machine Learning with Scikit-Learn, Packt Publishing Ltd."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Zhang, T., Yang, Y., Zeng, Y., and Zhao, Y. (2020). Cognitive Template-Clustering Improved LineMod for Efficient Multi-object Pose Estimation. Cognitive Computation, Springer.","DOI":"10.1007\/s12559-020-09717-5"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Tjaden, H., Schwanecke, U., and Schomer, E. (2017, January 22\u201329). Real-time monocular pose estimation of 3D objects using temporally consistent local color histograms. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.23"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/2\/613\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T14:14:57Z","timestamp":1760364897000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/2\/613"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,13]]},"references-count":61,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2022,1]]}},"alternative-id":["s22020613"],"URL":"https:\/\/doi.org\/10.3390\/s22020613","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,13]]}}}