A Survey on Infrared Thermography Based Automatic Electrical Fault Diagnosis Techniques
Infrared thermography is a non-contact and non-destructive technique for electrical equipment monitoring and fault diagnostics. It has been widely used since it can inspect the condition of electrical equipment and detect possible faults without needing to disconnect the equipment from its normal operation. Fault diagnosis is performed through the analysis of thermal images captured by a thermal camera. Manual analysis of thermogram for diagnosing the status of equipment needs to be carried out by a trained personal and this may take a lot of time. It may also prone to human error in the diagnosis. To overcome this, there are several researches focus on the development of methods for automatic electrical fault diagnostics. Most of the methods combine image processing and computational intelligence techniques in the diagnosis. Due to the large variability of equipment and diverse fault conditions, the diagnosis task could be very challenging. There are different techniques being proposed in the literature. This paper presents a survey on the current techniques for automatic electrical fault diagnostics based on infrared thermography.
KeywordsInfrared thermography Thermal imaging Electrical fault detection Intelligent fault diagnosis
This research was supported by Universiti Sains Malaysia Research University Grant (RUI 1001/PELECT/8014053) and Bridging Grant (304/PELECT/6316118).
- 2.Resendiz-Ochoa, E., Osornio-Rios, R.A., Benitez-Rangel, J.P., Morales-Hernandez, L.A., Romero-Troncoso, R.d.J.: Segmentation in thermography images for bearing defect analysis in induction motors. In: 2017 IEEE 11th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED), vol. 6 (2017)Google Scholar
- 3.Chou, Y.C., Yao, L.: Automatic diagnostic system of electrical equipment using infrared thermography. In: International Conference of Soft Computing and Pattern Recognition, 2009. SOCPAR’09, pp. 155–160 (2009)Google Scholar
- 4.Suguna, M., Roomi, S.M.M., Sanofer, I.: Fault localisation of electrical equipments using thermal imaging technique. In: 2016 International Conference on Emerging Technological Trends (ICETT), pp. 1–3 (2016)Google Scholar
- 6.Vollmer, M., Möllmann, K.-P., Infrared Thermal Imaging: Fundamentals, Research and Applications. Wiley (2010)Google Scholar
- 9.Ramírez-Rozo, T.J., García-Álvarez, J.C., Castellanos-Domínguez, C.G.: Infrared thermal image segmentation using expectation-maximization-based clustering. In: 2012 XVII Symposium of Image, Signal Processing, and Artificial Vision (STSIVA), pp. 223–226 (2012)Google Scholar
- 10.Herrera-Arellano, M.A., Terol-Villalobos, I.R., Morales-Hemandez, L.A., Valtierra-Rodriguez, M.: Infrared thermography-based automatic assessment of control components for electric machines. In: 2017 IEEE 11th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED), pp. 578–584 (2017)Google Scholar
- 11.NguYen, H.V., Tran, L.H.: Application of graph segmentation method in thermal camera object detection. In: 2015 20th International Conference on Methods and Models in Automation and Robotics (MMAR), pp. 829–833 (2015)Google Scholar
- 12.Dutta, T., Sil, J., Chottopadhyay, P.: Condition monitoring of electrical equipment using thermal image processing. In: 2016 IEEE First International Conference on Control, Measurement and Instrumentation (CMI), pp. 311–315 (2016)Google Scholar
- 13.Hurley, T.J.: Infrared qualitative and quantitative inspections for electrical utilities. In: Proceedings of the SPIE, Thermosense XII: An International Conference on Thermal Sensing and Imaging Diagnostic Applications, pp. 6–24 (1990)Google Scholar
- 15.Guo, L., Liu, S., Lv, M., Ma, J., Xie, L., Yang, Q.: Analysis on internal defects of electrical equipments in substation using heating simulation for infrared diagnose. In: 2014 China International Conference on Electricity Distribution (CICED), pp. 39–42 (2014)Google Scholar
- 17.Hui, Z., Fuzhen, H.: An intelligent fault diagnosis method for electrical equipment using infrared images. In: Proceedings of the 34th Chinese Control Conference, pp. 6372–6376, HangZhou, China (2015)Google Scholar
- 18.Ahmed, M.M., Huda, A.S.N., and Mat Isa, N.A.: Recursive construction of output-context fuzzy systems for the condition monitoring of electrical hotspots based on infrared thermography. Eng. Appl. Artif. Intell. 39, 120–131 (2015)Google Scholar