Infrared Thermography in Maritime Systems: A Systematic Review
Source abstract
Abstract
The maritime industry is undergoing a digital transformation, in which predictive maintenance and intelligent diagnostics play a crucial role in enhancing operational safety and efficiency. This paper investigates the application of infrared thermography (IRT) for fault detection and condition monitoring of ship machinery, with particular emphasis on its integration within condition-based and predictive maintenance frameworks. A systematic review was conducted in accordance with the PRISMA 2020 methodology, analyzing 210 publications retrieved from the Web of Science (WoS), Scopus, and Google Scholar databases to identify prevailing technological trends and research gaps. The results indicate that IRT enables early detection of critical faults such as overheating, insulation degradation, and poor electrical connections, thereby reducing unplanned downtime and improving system reliability. When integrated with artificial intelligence (AI), deep learning (DL), and convolutional neural networks (CNNs), diagnostic accuracy can be automated through enhanced data interpretation. Despite its proven effectiveness, standardized protocols and real-world validation of IRT–AI systems remain limited in the maritime sector. IRT is therefore recognized as a key enabler of safer, smarter, and more sustainable ship maintenance within the broader maritime digitalization framework.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Thermography and Photoacoustic Techniques · Maritime Transport Emissions and Efficiency · Machine Fault Diagnosis Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Faculty of Maritime Studies, University of Split, 21000 Split, Croatia
References (85)
Show source-provided references
1. Experimental validation of new approach for waste heat recovery from combustion engine for cooling and heating demands from combustion engine for maritime applications10.1016/j.jclepro.2020.125206 ↗
2. (2025, November 18). IMO 2020–Cutting Sulphur Oxide Emissions. Available online: https://www.imo.org/en/mediacentre/hottopics/pages/sulphur-2020.aspx.
3. European Commission Brussels (2025, November 17). Commission Publishes First Annual EU Report on CO2 Emissions from Maritime Transport. Available online: https://ec.europa.eu/clima/news-your-voice/news/commission-publishes-first-annual-eu-report-co2-emissions-maritime-transport-2020-05-25_en.
4. (2025, November 18). 2023 IMO Strategy on Reduction of GHG Emissions from Ships. Available online: https://www.imo.org/en/ourwork/environment/pages/2023-imo-strategy-on-reduction-of-ghg-emissions-from-ships.aspx.
5. (2008). Condition Monitoring and Diagnostics and Diagnostics of Machines–Thermography Part 1: General Procedures (Standard No. ISO 18434-1:2008). Available online: https://www.iso.org/standard/41648.html.
6. (2022). Thermal Insulation–Heat Transfer by Radiation–Vocabulary (Standard No. ISO 9288:2022). Available online: https://www.iso.org/obp/ui/#iso:std:iso:9288:en/.
7. (2008). Industrial Process Control Devices–Radiation Thermometers–Part 1: Technical Data for Radiation Thermometers (Standard No. IEC TS 62492-1:2008). Available online: https://webstore.iec.ch/en/publication/7103/.
8. Venegas, P., Ivorra, E., Ortega, M., and Saez de Ocariz, I. (2022). Towards the Automation of Infrared Thermography Inspection for Industrial Maintenace Applications. Sensors, 22.10.3390/s22020613 ↗
9. Ortega, M., Ivorra, E., Juan, A., Venegas, P., Martinez, J., and Alcaniz, M. (2021). MANTRA: AN Effective System Based on Augmented Reality and Infrared Thermography for Industrial Maintenance. Appl. Sci., 11.10.3390/app11010385 ↗
10. Melnyk, O., Onyschchenko, S., Onishchenko, O., Lohinov, O., and Ocheretna, V. (2023). Integral Approach to Vulnerability Assessment of Ship’s Critical Equipment and Systems. Trans. Marit. Sci., 12.10.7225/toms.v12.n01.002 ↗
11. Krystosik-Gromadzińska, A. (2019). Affordable hybrid thermography for merchant vessel engine room fire safety. Sci. J. Marit. Univ. Szczec., 21–26.
12. Review of maintenance strategies for ship machinery systems10.1080/20464177.2023.2180831 ↗
13. Balakrishnan, G.K., Yaw, C.T., Koh, S.P., Abedin, T., Raj, A.A., Tiong, S.K., and Chen, C.P. (2022). A Review of Infrared Thermography for Condition-Based Monitoring in Electrical Energy: Applications and Recommendations. Energies, 15.10.3390/en15166000 ↗
14. Godoy, D.R., Mavrakis, C., Mena, R., Kristjanpoller, F., and Viveros, P. (2024). An Advanced Framework for Predictive Maintenance Decisions: Integrating the Proportional Hazards Model and Machine Learning Techniques under CBM Multi-Covariate Scenarios. Appl. Sci., 14.10.3390/app14135514 ↗
15. Ali, A., and Abdelhadi, A. (2022). Condition-Based Monitoring and Maintenance: State of the Art Review. Appl. Sci., 12.10.3390/app12020688 ↗
16. Achouch, M., Dimitrova, M., Ziane, K., Sattarpanah Karganroudi, S., Dhouib, R., Ibrahim, H., and Adda, M. (2022). On Predictive Maintenance in Industry 4.0: Overview, Models, and Challenges. Appl. Sci., 12.10.3390/app12168081 ↗
17. Ullah, I., Yang, F., Khan, R., Liu, L., Yang, H., Gao, B., and Sun, K. (2017). Predictive Maintenance of Power Substation Equipment by Infrared Thermography Using a Machine-Learning Approach. Energies, 10.10.3390/en10121987 ↗
18. Trejo-Chavez, O., Cruz-Albarran, I.A., Resendiz Ochoa, E., Salinas-Aguilar, A., Morales-Hernandez, L., Basurto-Hurtado, J.A., and Perez-Ramirez, C.A. (2023). A CNN-Based Methodology for Identifying Mechanical Faults in Induction Motors Using Thermography. Machines, 11.10.3390/machines11070752 ↗
19. Lu, D., Tang, H., Teng, L., Tan, J., Wang, M., Tian, Z., and Wang, L. (2024). Multiscale Feature-Based Infrared Ship Detection. Appl. Sci., 14.10.3390/app14010246 ↗
20. Recent industrial applications of infrared thermography: A review10.1109/TII.2018.2884738 ↗
21. Monieta, J. (2018, January 30). The use of thermography in the diagnosis of ship piston internal combustion engines, MATEC Web of Conferences. Proceedings of the 17th International Conference Diagnostics of Machines and Vehicles, Les Ulis, France.10.1051/matecconf/201818201027 ↗
22. Lopez-Perez, D., and Antonino-Daviu, J. (2016, January 23–26). Detection of mechanical faults in induction machines with infrared thermography: Field cases. Proceedings of the IECON 2016—42nd Annual Conference of the IEEE Industrial Electronics Society, Florence, Italy.10.1109/IECON.2016.7793604 ↗
23. Rotating machinery fault diagnosis based on convolutional neural network and infrared thermal imaging10.1016/j.cja.2019.08.014 ↗
24. Wang, X., Si, S., Li, Y., and Li, Y. (2018, January 15–17). A fault diagnosis method for rotating machinery under variable speed condition based on infrared thermography. Proceedings of the 2018 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC), Xi’an, China.10.1109/SDPC.2018.8664850 ↗
25. Resendiz-Ochoa, E., Morales-Hernandez, L.A., Cruz-Albarran, I.A., and Alvarez-Junco, S. (2022, January 9–11). Induction Motor Failure Analysis using Machine Learning and Infrared Thermography. Proceedings of the 2022 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC), Ixtapa, Mexico.10.1109/ROPEC55836.2022.10018653 ↗
26. Conditional generative adversarial network based data augmentation for fault diagnosis of diesel engines applied with infrared thermography and deep convolutional neural network
27. The PRISMA 2020 statement: An uploaded guideline for reporting systematic reviews10.1136/bmj.n71 ↗
28. Bojić, F., Gudelj, A., and Bošnjak, R. (2022). Port-Related Shipping Gas Emissions–A Systematic Review of Research. Appl. Sci., 12.10.3390/app12073603 ↗
29. Karin, I., Golub Medvešek, I., and Šoda, J. (2025). Best-Suited Communication Technology for Maritime Signaling Facilities: A Literature Review. Appl. Sci., 15.10.3390/app15073452 ↗
30. Big data and artificial intelligence in the maritime industry: A bibliometric review and future research directions10.1080/03088839.2020.1788731 ↗
31. Ferrarini, L., Filippopoulos, Y., and and Lajic, Z. (2025). Digital Transformation in the Shipping Industry: A Network-Based Bibliometric Analysis. J. Mar. Sci. Eng., 13.10.3390/jmse13050894 ↗
32. Thermography-Based Methodology for Multifault Diagnosis on Kinematic Chain10.1109/TII.2018.2816925 ↗
33. IRTCog: Fault Diagnosis of Rotor-Bearing System Based on Modified Transfer Model With Variable Visual Angle Thermal Images
34. Image-based incipient fault classification of electrical substation equipment by transfer learning of deep convolutional neural network10.1109/ICJECE.2021.3109293 ↗
35. Gugaliya, A., Singh, G., and Naikan, V.N.A. (2018, January 18–20). Effective combination of motor fault diagnosis techniques. Proceedings of the International Conference on Power, Instrumentation Control and Computing (PICC), Thrissur, India.10.1109/PICC.2018.8384812 ↗
36. A Rotating Machinery Fault Diagnosis Method Based on Feature Learning of Thermal Images10.1109/ACCESS.2019.2893331 ↗
37. Lopez-Perez, D., and Antonino-Daviu, J. (2016, January 4–7). Application of infrared thermography to fault detection in industrial induction motors: Case stories. Proceedings of the 2016 XXII International Conference on Electrical Machines (ICEM), Lausanne, Switzerland.10.1109/ICELMACH.2016.7732823 ↗
38. Malyk, B., and Malyk-Zamorii, S. (2017, January 10–13). Thermography Testing of Telecommunication Facilities Temperature Conditions. Proceedings of the 2017 4th International Scientific-Practical Conference Problems of Infocommunications. Science and Technology (PIC S&T), Kharkov, Ukraine.10.1109/INFOCOMMST.2017.8246443 ↗
39. Mechkov, E. (2017, January 1–3). Application of Infrared Thermography Technique in Transformers Maintenance in Distribution Network. Proceedings of the 2017 15th International Conference on Electrical Machines, Drives and Power Systems (ELMA), Sofia, Bulgaria.10.1109/ELMA.2017.7955462 ↗
40. Multi-Sensor Fault Diagnosis for Misalignment and Unbalance Detection Using Machine Learning10.1109/TIA.2023.3286833 ↗
41. Ramirez-Nunez, J.A., Morales-Hernandez, L.A., Osornio-Rios, R.A., Antonino-Daviu, J.A., and Romero-Troncoso, R.J. (2016, January 23–26). Self-adjustment Methodology of a Thermal Camera for Detecting Faults in Industrial Machinery. Proceedings of the IECON 2016-42nd Annual Conference of the IEEE Industrial Electronics Society, Florence, Italy.10.1109/IECON.2016.7793158 ↗
42. Redon, P., Romero-Troncoso, R.J., Picazo-Rodenas, M.J., and Antonino-Daviu, J. (September, January 29). Reliable methodology for online fault diagnosis in induction motors using passive infrared thermography. Proceedings of the 2017 IEEE 11th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED), Tinos, Greece.10.1109/DEMPED.2017.8062416 ↗
43. Resendiz-Ochoa, E., Enriquez-Ugalde, J.M., Saucedo-Dorantes, J.J., and Morales-Hernandez, L.A. (2021, January 22–25). Broken Rotor Bar Failures Diagnosis with Supervised Learning and Infrared Thermography. Proceedings of the 2021 IEEE 13th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED), Dallas, TX, USA.10.1109/SDEMPED51010.2021.9605504 ↗
44. Resendiz-Ochoa, E., Osornio-Rios, R.A., Benitez-Rangel, J.P., Morales-Hernandez, L.A., and Romero-Troncoso, R.J. (September, January 29). Segmentation in Thermography Images for Bearing Defect Analysis in Induction Motors. Proceedings of the 2017 IEEE 11th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED), Tinos, Greece.10.1109/DEMPED.2017.8062412 ↗
45. Autonomous Smart-Edge Fault Diagnostics via Edge-Cloud-Orchestrated Collaborative Computing for Infrared Electrical Equipment Images10.1109/JSEN.2024.3415639 ↗
46. Passive Thermography Based Bearing Fault Diagnosis Using Transfer Learning With Varying Working Conditions10.1109/JSEN.2022.3164430 ↗
47. Choung, Y., Lee, S., and Kim, W. (2021). Latest advances in common signal processing of pulsed thermography for enhanced detectability: A review. Appl. Sci., 11.10.3390/app112412168 ↗
48. Fanchiang, K., Huang, Y., and Kuo, C. (2021). Power Electric Transformer Fault Diagnosis Based on Infrared Thermal Images Using Wasserstein Generative Adversarial Networks and Deep Learning Classifier. Electronics, 10.10.3390/electronics10101161 ↗
49. Guo, Y., Zhang, Y., Sun, B., and Wang, Y. (2023). Adversarial deep transfer learning in fault diagnosis: Progress, challenges, and future prospects. Sensors, 23.10.3390/s23167263 ↗
50. Hou, F., Zhang, Y., Zhou, Y., Zhang, M., and Wu, J. (2022). Review on infrared imaging technology. Sustainability, 14.10.3390/su141811161 ↗
51. Li, K., Tian, G.Y., and Ahmed, J. (2023). Emissivity Correction and Thermal Pattern Reconstruction in Eddy Current Pulsed Thermograph. Sensors, 23.10.3390/s23052646 ↗
52. Ucar, A., Karakose, M., and Kirimca, N. (2024). Artificial intelligence for predictive maintenance applications: Key components, trustworthiness, and future trends. Appl. Sci., 14.10.3390/app14020898 ↗
53. Evaluation of Reliability of Large Hybrid Curvic Gear Using Thermography
54. A New Intelligent Fault Diagnosis Method of Rotating Machinery under Varying-Speed Conditions Using Infrared Thermography10.1155/2019/2619252 ↗
55. Vibration and infrared thermography based multiple fault diagnosis of bearing using deep learning10.1080/10589759.2022.2118747 ↗
56. Research on thermal imaging fault detection system based on Weibull distributed electrical system10.1088/1742-6596/1941/1/012037 ↗
57. Possibility of defect detection by eddy current thermography in marine structures
58. Infrared thermography based fault diagnosis of diesel engines using convolutional neural network and image enhancement10.1515/phys-2024-0110 ↗
59. Exploring the Diagnostic Potential of Infrared Thermography for Experimental Assessment of Cavitation and Air Entrainment-induced Faults in Centrifugal Pumps
60. The future of condition based monitoring: Risks of operator removal on complex platforms10.1007/s00146-022-01521-z ↗
61. Bampoula, X., Nikolakis, N., and Alexopoulos, K. (2024). Condition Monitoring and Predicitive Maintenance of Assets in Manufacturing Using LSTM-Autoencoders and Transformer Encoders. Sensors, 24.10.3390/s24103215 ↗
62. Garcia, J., Rios-Colque, L., Pena, A., and Rojas, L. (2025). Condition Monitoring and Predicitive Maintenance in Industrial Equipment: An NLP-Assisted Review of Signal Processing, Hybrid Models, and Implementation Challenges. Appl. Sci., 15.10.3390/app15105465 ↗
63. Moleda, M., Malysiak-Mrozek, B., Ding, W., Sunderam, V., and Mrozek, D. (2023). From Corrective to Predictive Maintenance–A Review of Maintenance Approaches for the Power Industry. Sensors, 23.10.3390/s23135970 ↗
64. Riccio, C., Menanno, M., Zennaro, I., and Savino, M.M. (2024). A New Methodological Framework for Optimizing Predicitve Maintenance Using Machine Learning Combined with Product Quality Parameters. Machines, 12.10.3390/machines12070443 ↗
65. Briguglio, G., and Crupi, V. (2024). Review on Sensors for Sustainable and Safe Maritime Mobility. J. Mar. Sci. Eng., 12.10.3390/jmse12020353 ↗
66. Maione, F., Lino, P., Maione, G., and Giannino, G. (2024). A Machine Learning Framework for Condition-Based Maintenance of Marine Diesel Engines: A Case Study. Algorithms, 17.10.20944/preprints202407.0941.v1 ↗
67. Sutherland, N., Marsh, S., Priestnall, G., Bryan, P., and Mills, J. (2023). InfraRed Thermography and 3D- Data Fusion for Architectual Heritage: A Scoping Review. Remote Sens., 15.10.3390/rs15092422 ↗
68. Pušnik, I., and Geršak, G. (2021). Evaluation of the Size-of-Source Effect in Thermal Imaging Cameras. Sensors, 21.10.3390/s21020607 ↗
69. Gallardo-Saavedra, S., Hernandez-Callejo, L., del Carmen Alonso-Garcia, M., Munoz-Cruzado-Alba, J., and Ballestin-Fuertes, J. (2020). Infrared Thermography for the Detection and Characterization of Photovoltaic Defects: Comparison between Illumination and Dark Conditions. Sensors, 20.10.3390/s20164395 ↗
70. Loche, M., Scaringi, G., Blahut, J., and Hartvich, F. (2022). Investigating the Potential of Infrared Thermography to Inform on Physical and Mechanical Properties of Soils for Geotechnical Engineering. Remote Sens., 14.10.3390/rs14164067 ↗
71. Alvarado-Hernandez, A.I., Zamudio-Ramirez, I., Jean-Cuellar, A.Y., Osornio-Rios, R.A., Donderis-Quiles, V., and Antonino-Daviu, J.A. (2022). Infrared Thermography Smart Sensor for the Condition Monitoring of Gearbox and Bearing Faults in Induction Motors. Sensors, 22.10.3390/s22166075 ↗
72. Radiometric Infrared Thermography of Solar Photovoltaic Systems: An Explainable Predictive Maintenance Approach for Remote Aerial Diagnostic Monitoring10.3390/smartcities7030053 ↗
73. Angrisani, L., De Benedetto, E., Duraccio, L., Lo Regio, F., Ruggiero, R., and Tedesco, A. (2023). Infrared Thermography for Real-Time Assessment of the Effectiveness of Scoliosis Braces. Sensors, 23.10.3390/s23198037 ↗
74. Korelidou, V., Simitzis, P., Massouras, T., and Gelasakis, A.I. (2024). Infrared Thermography as a Diagnostic Tool for the Assessment of Mastitis in Dairy Ruminants. Animals, 14.10.3390/ani14182691 ↗
75. Ma, Y., Vien, B.S., Kuen, T., and Chiu, W.K. (2024). Clustering-Based Thermography for Detecting Multiple Substances Under Large-Scale Floating Covers. Sensors, 24.10.3390/s24248030 ↗
76. Ebrahimi, S., Fleuret, J., Klein, M., Therodux, L.-D., Georges, M., Ibarra-Castanedo, C., and Maldaque, X. (2021). Robust Principal Component Thermography for Defect Detection in Composites. Sensors, 21.10.3390/s21082682 ↗
77. Wei, Z., Fernandes, H., Herrmann, H.-G., Tarpani, J.R., and Osman, A. (2021). A Deep Learning Method for the Impact Damage Segmentation of Curve-Shaped CFRP Specimens Inspected by Infrared Thermography. Sensors, 21.10.3390/s21020395 ↗
78. Cabizosu, A., Grotto, D., Lopez Lopez, A., and Castaneda Vozmediano, R. (2024). Thermography Sensor to Assess Motor and Sensitive Neuromuscular Sequels of Brain Damage. Sensors, 24.10.3390/s24061723 ↗
79. Alves, R., van Meulen, F., Overeem, S., Zinger, S., and Stuijk, S. (2024). Thermal Cameras for Continuous and Contactless Respiration Monitoring. Sensors, 24.10.3390/s24248118 ↗
80. Faus Camarena, M., Izquierdo-Renau, M., Julian-Rochina, I., Arrebola, M., and Miralles, M. (2024). Update on the Use of Infrared Thermography in the Early Detection of Diabetic Foot Complications: A Bibliographic Review. Sensors, 24.10.20944/preprints202310.1516.v1 ↗
81. (2025, October 19). Applied Sciences Special Issue: Recent Progress in Infrared Thermography. Available online: https://www.mdpi.com/journal/applsci/special_issues/8H48M7D458.
82. (2025, October 22). Special Issue/Topical Collection: Big Data and AI Approaches for Infrared Thermography Inspection. Available online: https://www.mdpi.com/journal/applsci/special_issues/Infrared_Inspection.
83. Vollmer, M., and Möllmann, K.-P. (2010). Infrared Thermal Imaging Fundamentals, Research and Applications, Wiley.10.1002/9783527630868 ↗
84. Hidayat, Z., Avdelidis, N.P., and Fernandesm, H. (2025). Brief Review of Vibrothermography and Optical Thermography for Defect Quantification in CFRP Material. Sensors, 25.10.3390/s25061847 ↗
85. Monitoring changes in hydric properties of treated stone material with conservation products by time-sequential IR thermography