Towards the Automation of Infrared Thermography Inspections for Industrial Maintenance Applications
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 f...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-01-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/2/613 |
| _version_ | 1797490484348715008 |
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| author | Pablo Venegas Eugenio Ivorra Mario Ortega Idurre Sáez de Ocáriz |
| author_facet | Pablo Venegas Eugenio Ivorra Mario Ortega Idurre Sáez de Ocáriz |
| author_sort | Pablo Venegas |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T00:33:39Z |
| format | Article |
| id | doaj.art-a9e7cf7ca6ed46e99fc07007e9fab18c |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T00:33:39Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-a9e7cf7ca6ed46e99fc07007e9fab18c2023-11-23T15:21:31ZengMDPI AGSensors1424-82202022-01-0122261310.3390/s22020613Towards the Automation of Infrared Thermography Inspections for Industrial Maintenance ApplicationsPablo Venegas0Eugenio Ivorra1Mario Ortega2Idurre Sáez de Ocáriz3Aeronautical Technologies Centre (CTA), 01510 Miñano, SpainInstitute for Research and Innovation in Bioengineering, Polytechnic University of Valencia, 46022 Valencia, SpainInstitute for Research and Innovation in Bioengineering, Polytechnic University of Valencia, 46022 Valencia, SpainAeronautical Technologies Centre (CTA), 01510 Miñano, SpainThe 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.https://www.mdpi.com/1424-8220/22/2/613infrared thermographymaintenanceindustrial equipmentmachine learning |
| spellingShingle | Pablo Venegas Eugenio Ivorra Mario Ortega Idurre Sáez de Ocáriz Towards the Automation of Infrared Thermography Inspections for Industrial Maintenance Applications Sensors infrared thermography maintenance industrial equipment machine learning |
| title | Towards the Automation of Infrared Thermography Inspections for Industrial Maintenance Applications |
| title_full | Towards the Automation of Infrared Thermography Inspections for Industrial Maintenance Applications |
| title_fullStr | Towards the Automation of Infrared Thermography Inspections for Industrial Maintenance Applications |
| title_full_unstemmed | Towards the Automation of Infrared Thermography Inspections for Industrial Maintenance Applications |
| title_short | Towards the Automation of Infrared Thermography Inspections for Industrial Maintenance Applications |
| title_sort | towards the automation of infrared thermography inspections for industrial maintenance applications |
| topic | infrared thermography maintenance industrial equipment machine learning |
| url | https://www.mdpi.com/1424-8220/22/2/613 |
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