Defect Detection Using Thermography Camera Techniques: A review
pdf

How to Cite

Defect Detection Using Thermography Camera Techniques: A review. (2024). Al-Khwarizmi Engineering Journal, 20(4), 70-88. https://doi.org/10.22153/kej.2024.03.002

Publication Dates

Received

2023-07-13

Revised

2024-02-24

Accepted

2024-03-11

Abstract

Individuals across different industries, including but not limited to agriculture, drones, pharmaceuticals and manufacturing, are increasingly using thermal cameras to achieve various safety and security goals. This widespread adoption is made possible by advancements in thermal imaging sensor technology. The current literature provides an in-depth exploration of thermography camera applications for detecting faults in sectors such as fire protection, manufacturing, aerospace, automotive, non-destructive testing and structural material industries. The current discussion builds on previous studies, emphasising the effectiveness of thermography cameras in distinguishing undetectable defects by the human eye. Various methods for defect detection, including temperature analysis and image processing algorithms, are thoroughly presented. The factors contributing to the effectiveness of thermography cameras are explored, along with their advantages over traditional inspection methods. The literature review highlights the diverse applications of thermography cameras in fault detection. The review highlights the remarkable transformation brought by thermal camera technology in mechanical system fault detection, leading to improved maintenance practices. These cameras can detect unseen irregularities, enable non-invasive testing and support hands-on system maintenance, making them indispensable tools for ensuring mechanical systems operate efficiently, reliably and safely. With the continuous advancement of technology, the integration of Industry 4.0 and IoT technologies will further enhance the capabilities of thermal cameras, ensuring elevated performance across different domains. In electrical systems, thermal cameras allow for the early identification of faults, enabling proactive maintenance to mitigate risks. Additionally, by assessing structural integrity, thermal cameras can detect thermal and insulation inefficiencies, leading to improved energy efficiency.

 

pdf

References

Ali Sarhadi, Rodrigo Q. Albuquerque, Martin Demleitner, Holger Ruckdäschel, Martin A. Eder, Machine learning based thermal imaging damage detection in glass-epoxy composite materials, Composite Structures, Volume 295, 2022, 115786, ISSN 0263-8223, https://doi.org/10.1016/j.compstruct.2022.115786.

Péter Kovács, Bernhard Lehner, Gregor Thummerer, Günther Mayr, Peter Burgholzer, Mario Huemer; Deep learning approaches for thermographic imaging. Journal of Applied Physics 21 October 2020; 128 (15): 155103. https://doi.org/10.1063/5.0020404.

Youngjun Cho, Nadia Bianchi-Berthouze, Nicolai Marquardt, and Simon J. Julier. 2018. Deep Thermal Imaging: Proximate Material Type Recognition in the Wild through Deep Learning of Spatial Surface Temperature Patterns. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (CHI '18). Association for Computing Machinery, New York, NY, USA, Paper 2, 1–13. https://doi.org/10.1145/3173574.3173576.

C. N. Naga Priya, S. D. Ashok, Bhanshidar Maji, and K. S. Kumaran, “Deep Learning Based Thermal Image Processing Approach for Detection of Buried Objects and Mines”, Eng. J., vol. 25, no. 3, pp. 61-67, Mar. 2021.

O. Janssens, R. Van de Walle, M. Loccufier and S. Van Hoecke, "Deep Learning for Infrared Thermal Image Based Machine Health Monitoring," in IEEE/ASME Transactions on Mechatronics, vol. 23, no. 1, pp. 151-159, Feb. 2018, doi: 10.1109/TMECH.2017.2722479.

M. Najafi, Y. Baleghi, S. A. Gholamian and S. Mehdi Mirimani, "Fault Diagnosis of Electrical Equipment through Thermal Imaging and Interpretable Machine Learning Applied on a Newly-introduced Dataset," 2020 6th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS), Mashhad, Iran, 2020, pp. 1-7, doi: 10.1109/ICSPIS51611.2020.9349599.

Chen, C.; Chandra, S.; Han, Y.; Seo, H. Deep Learning-Based Thermal Image Analysis for Pavement Defect Detection and Classification Considering Complex Pavement Conditions. Remote Sens. 2022, 14, 106. https://doi.org/10.3390/rs14010106.

Najah, A., Mustafa, F. F., & Hacham, W. S. (2021). Building a High Accuracy Transfer Learning-Based Quality Inspection System at Low Costs. Al-Khwarizmi Engineering Journal, 17(1), 1–12. https://doi.org/10.22153/kej.2021.12.001.

Tsai, P.-F.; Liao, C.-H.; Yuan, S.-M. Using Deep Learning with Thermal Imaging for Human Detection in Heavy Smoke Scenarios. Sensors 2022, 22, 5351. https://doi.org/10.3390/s22145351.

Mary, Ali Hussien, Zubaidah Bilal Kadhim, and Zainab Saad Sharqi. "Face recognition and emotion recognition from facial expression using deep learning neural network." IOP Conference Series: Materials Science and Engineering. Vol. 928. No. 3. IOP Publishing, 2020.‏

Chandra, S.; AlMansoor, K.; Chen, C.; Shi, Y.; Seo, H. Deep Learning Based Infrared Thermal Image Analysis of Complex Pavement Defect Conditions Considering Seasonal Effect. Sensors 2022, 22, 9365. https://doi.org/10.3390/s22239365.

Hardan, F., & R. J. Almusawi, A. . (2022). Developing an Automated Vision System for Maintaing Social Distancing to Cure the Pandemic. Al-Khwarizmi Engineering Journal, 18(1), 38–50. https://doi.org/10.22153/kej.2022.03.002.

Chiara Filippini, David Perpetuini, Daniela Cardone, Antonio Maria Chiarelli and Arcangelo Merla, Thermal infrared imaging and artificial intelligence techniques can support mild Alzheimer disease diagnosis, CEUR Workshop Proceedings, Vol-2804, 2020.

A. Guedidi, A. Guettaf, A. J. M. Cardoso, W. Laala and A. Arif, "Bearing Faults Classification Based on Variational Mode Decomposition and Artificial Neural Network," 2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED), Toulouse, France, 2019, pp. 391-397, doi: 10.1109/DEMPED.2019.8864830.

Min Hao, "Characteristics of misalignment fault in rotor systems based on frequency analysis," 2011 International Conference on Computer Science and Service System (CSSS), Nanjing, China, 2011, pp. 3893-3895, doi: 10.1109/CSSS.2011.5972166.

H. -C. Lee, Y. -C. Chang and Y. -S. Huang, "A Reliable Wireless Sensor System for Monitoring Mechanical Wear-Out of Parts," in IEEE Transactions on Instrumentation and Measurement, vol. 63, no. 10, pp. 2488-2497, Oct. 2014, doi: 10.1109/TIM.2014.2312498.

F. Imad, F. Nagi and S. K. Ahmed, "Investigating lubrication properties using pulse-echo ultrasound technique," 2012 IEEE International Conference on Control System, Computing and Engineering, Penang, Malaysia, 2012, pp. 166-170, doi: 10.1109/ICCSCE.2012.6487135.

Achouch, M.; Dimitrova, M.; Ziane, K.; SattarpanahKarganroudi, S.; Dhouib, R.; Ibrahim, H.; Adda, M. On Predictive Maintenance in Industry 4.0: Overview, Models, and Challenges. Appl. Sci. 2022, 12, 8081. https://doi.org/10.3390/app12168081.

Grujić, K. A Review of Thermal Spectral Imaging Methods for Monitoring High-Temperature Molten Material Streams. Sensors 2023, 23, 1130. https://doi.org/10.3390/s23031130.

Georgios Tsaramirsis, Antreas Kantaros, Izzat Al-Darraji, Dimitrios Piromalis, Charalampos Apostolopoulos, Athanasia Pavlopoulou, Muath Alrammal, Zamhar Ismail, Seyed M. Buhari, Milos Stojmenovic, HatemTamimi, PrincyRandhawa, Akshet Patel, FazalQudus Khan, "A Modern Approach towards an Industry 4.0 Model: From Driving Technologies to Management", Journal of Sensors, vol. 2022, Article ID 5023011, 18 pages, 2022. https://doi.org/10.1155/2022/5023011.

X. Zhou and T. Schoepf, "Characteristics of Overheated Electrical Joints Due to Loose Connection," 2011 IEEE 57th Holm Conference on Electrical Contacts (Holm), Minneapolis, MN, USA, 2011, pp. 1-7, doi: 10.1109/HOLM.2011.6034795.

B. Wei, A. Marzàbal, J. Perez, R. Pinyol, J. M. Guerrero and J. C. Vásquez, "Overload and Short-Circuit Protection Strategy for Voltage Source Inverter-Based UPS," in IEEE Transactions on Power Electronics, vol. 34, no. 11, pp. 11371-11382, Nov. 2019, doi: 10.1109/TPEL.2019.2898165.

M. A. Rahman and I. Pranoto, "Review on Current Thermal Issue and Cooling Technology Development on Electric Vehicles Battery," 2020 6th International Conference on Science and Technology (ICST), Yogyakarta, Indonesia, 2020, pp. 1-6, doi: 10.1109/ICST50505.2020.9732879.

Hongzhao Li, "Thermal Fault Detection and Diagnosis of Electrical Equipment Based on the Infrared Image Segmentation Algorithm", Advances in Multimedia, vol. 2021, Article ID 9295771, 7 pages, 2021. https://doi.org/10.1155/2021/9295771.

Sheng Han, Fan Yang, Gang Yang, Bing Gao, Na Zhang, Dawei Wang, Electrical equipment identification in infrared images based on ROI-selected CNN method, Electric Power Systems Research, Volume 188, 2020, 106534, ISSN 0378-7796, https://doi.org/10.1016/j.epsr.2020.106534.

Cubukcu, A. Akanalci, Real-time inspection and determination methods of faults on photovoltaic power systems by thermal imaging in Turkey, Renewable Energy, Volume 147, Part 1, 2020, Pages 1231-1238, ISSN 0960-1481, https://doi.org/10.1016/j.renene.2019.09.075.

Toughness and failure of volcanic edifices, Tectonophysics, Volume 471, Issues 1–2, 2009, Pages 27-35, ISSN 0040-1951, https://doi.org/10.1016/j.tecto.2009.03.001.

Zheda Zhu, Spencer E. Quiel, NegarElhamiKhorasani, Bivariate structural-fire fragility curves for simple-span overpass bridges with composite steel plate girders, Structural Safety, Volume 100, 2023, 102294, ISSN 0167-4730, https://doi.org/10.1016/j.strusafe.2022.102294.

ixin Yan, Ruotong Yao, Jingyuan Zhao, Kaili Chen, LirongDuan, Tian Wang, Shujun Zhang, Jinping Guan, ZhaozhuZheng, Xiaoqin Wang, Zekun Liu, Yi Li, Gang Li, Implantable nerve guidance conduits: Material combinations, multi-functional strategies and advanced engineering innovations, Bioactive Materials, Volume 11, 2022, Pages 57-76, ISSN 2452-199X, https://doi.org/10.1016/j.bioactmat.2021.09.030.

Ham, Y., Golparvar-Fard, M. 3D Visualization of thermal resistance and condensation problems using infrared thermography for building energy diagnostics. Vis. in Eng. 2, 12 (2014). https://doi.org/10.1186/s40327-014-0012-0

Shima Taheri, A review on five key sensors for monitoring of concrete structures, Construction and Building Materials, Volume 204, 2019, Pages 492-509, ISSN 0950-0618, https://doi.org/10.1016/j.conbuildmat.2019.01.172.

Al-Sahib, Nabeel K. Abid, Hussam K. Abdul Ameer, and SaifGhazy Faisal Ibrahim. "Monitoring and quality control of stud welding." Al-Khwarizmi Engineering Journal 5.1 (2009): 53-70.

Tao, Y.H.; Fitzgerald, A.J.; Wallace, V.P. Non-Contact, Non-Destructive Testing in Various Industrial Sectors with Terahertz Technology. Sensors 2020, 20, 712. https://doi.org/10.3390/s20030712.

A.A. Gowen, B.K. Tiwari, P.J. Cullen, K. McDonnell, C.P. O'Donnell, Applications of thermal imaging in food quality and safety assessment, Trends in Food Science & Technology, Volume 21, Issue 4, 2010, Pages 190-200, ISSN 0924-2244, https://doi.org/10.1016/j.tifs.2009.12.002.

Imani, F., Chen, R., Diewald, E., Reutzel, E., and Yang, H. (September 18, 2019). "Deep Learning of Variant Geometry in Layerwise Imaging Profiles for Additive Manufacturing Quality Control." ASME. J. Manuf. Sci. Eng. November 2019; 141(11): 111001. https://doi.org/10.1115/1.4044420.

Shuxian Nian, Tina Pham, Carl Haas, Nadine Ibrahim, Daeun Yoon, Hana Bregman, A functional demonstration of adaptive reuse of waste into modular assemblies for structural applications: The case of bicycle frames, Journal of Cleaner Production, Volume 348, 2022, 131162, ISSN 0959-6526, https://doi.org/10.1016/j.jclepro.2022.131162.

Peter Trampus, VjeraKrstelj, Giuseppe Nardoni, NDT integrity engineering – A new discipline, Procedia Structural Integrity, Volume 17, 2019, Pages 262-267, ISSN 2452-3216, https://doi.org/10.1016/j.prostr.2019.08.035.

Lixin Wu, Chengyu Cui, NaiguangGeng, Jinzhuang Wang, Remote sensing rock mechanics (RSRM) and associated experimental studies, International Journal of Rock Mechanics and Mining Sciences, Volume 37, Issue 6, 2000, Pages 879-888, ISSN 1365-1609, https://doi.org/10.1016/S1365-1609(99)00066-0.

Gamal ElMasry, Ramadan ElGamal, Nasser Mandour, Pere Gou, Salim Al-Rejaie, Etienne Belin, David Rousseau, Emerging thermal imaging techniques for seed quality evaluation: Principles and applications, Food Research International, Volume 131, 2020, 109025, ISSN 0963-9969, https://doi.org/10.1016/j.foodres.2020.109025.

MingjiangXie, Zhigang Tian, A review on pipeline integrity management utilizing in-line inspection data, Engineering Failure Analysis, Volume 92, 2018, Pages 222-239, ISSN 1350-6307, https://doi.org/10.1016/j.engfailanal.2018.05.010.

Park, S., Lim, H., Tamang, B. et al. A Preliminary Study on Leakage Detection of Deteriorated Underground Sewer Pipes Using Aerial Thermal Imaging. Int J Civ Eng 18, 1167–1178 (2020).

https://doi.org/10.1007/s40999-020-00521-8

AngelikiKylili, Paris A. Fokaides, PetrosChristou, Soteris A. Kalogirou, Infrared thermography (IRT) applications for building diagnostics: A review, Applied Energy, Volume 134, 2014, Pages 531-549, ISSN 0306-2619, https://doi.org/10.1016/j.apenergy.2014.08.005.

Adegboye, M.A.; Fung, W.-K.; Karnik, A. Recent Advances in Pipeline Monitoring and Oil Leakage Detection Technologies: Principles and Approaches. Sensors 2019, 19, 2548. https://doi.org/10.3390/s19112548.

S. Timashev and A. Bushinskaya, Diagnostics and Reliability of Pipeline Systems, vol. 30. 2016.

San-Miguel-Ayanz, J., Ravail, N. Active Fire Detection for Fire Emergency Management: Potential and Limitations for the Operational Use of Remote Sensing. Nat Hazards 35, 361–376 (2005). https://doi.org/10.1007/s11069-004-1797-2.

T.T. Aralt, A.R. Nilsen, Automatic fire detection in road traffic tunnels, Tunnelling and Underground Space Technology, Volume 24, Issue 1, 2009, Pages 75-83, ISSN 0886-7798, https://doi.org/10.1016/j.tust.2008.04.001.

Stephen Vidas, PeymanMoghadam, HeatWave: A handheld 3D thermography system for energy auditing, Energy and Buildings, Volume 66, 2013, Pages 445-460, ISSN 0378-7788, https://doi.org/10.1016/j.enbuild.2013.07.030.

Andoga, R.; Főző, L.; Schrötter, M.; Češkovič, M.; Szabo, S.; Bréda, R.; Schreiner, M. Intelligent Thermal Imaging-Based Diagnostics of Turbojet Engines. Appl. Sci. 2019, 9, 2253. https://doi.org/10.3390/app9112253

Aline Kirsten Vidal de Oliveira, MohammadrezaAghaei, Ricardo Rüther, Aerial infrared thermography for low-cost and fast fault detection in utility-scale PV power plants, Solar Energy, Volume 211, 2020, Pages 712-724, ISSN 0038-092X, https://doi.org/10.1016/j.solener.2020.09.066.

Abdulateef, O. F., & Salman, L. A. (2015). Thermal Field Analysis of Oblique Machining Process with Infrared Image for AA6063-T6. Al-Khwarizmi Engineering Journal, 11(1), 1–10.

O. Breitenstein, W. Warta, and M. Langenkamp, Lock-In Thermography: Basics and Use for Evaluating Electronic Devices and Materials. 2011.

Raja, BNK, Miramini, S, Duffield, C, Sofi, M, Mendis, P, Zhang, L. The influence of ambient environmental conditions in detecting bridge concrete deck delamination using infrared thermography (IRT). Struct Control Health Monit. 2020; https://doi.org/10.1002/stc.2506.

U. Sreedhar, C.V. Krishnamurthy, Krishnan Balasubramaniam, V.D. Raghupathy, S. Ravisankar, Automatic defect identification using thermal image analysis for online weld quality monitoring, Journal of Materials Processing Technology, Volume 212, Issue 7, 2012, Pages 1557-1566, ISSN 0924-0136, https://doi.org/10.1016/j.jmatprotec.2012.03.002.

C. Morales-Perez, J. Rangel-Magdaleno, H. Peregrina-Barreto, J. Ramirez-Cortes and E. Vazquez-Pacheco, "Bearing Fault Detection Technique by using Thermal Images: A case of Study," 2019 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), Auckland, New Zealand, 2019, pp. 1-6, doi: 10.1109/I2MTC.2019.8826953.

J. Yang, W. Wang, G. Lin, Q. Li, Y. Sun and Y. Sun, "Infrared Thermal Imaging-Based Crack Detection Using Deep Learning," in IEEE Access, vol. 7, pp. 182060-182077, 2019, doi: 10.1109/ACCESS.2019.2958264.

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright (c) 2024 Al-Khwarizmi Engineering Journal