Sensors Fusion and Multidimensional Point Cloud Analysis for Electrical Power System Inspection
Thermal inspection is a powerful tool that enables the diagnosis of several components at its early stages. One critical aspect that influences thermal inspection outputs is the infrared reflection from external sources. This situation may change the readings, demanding that an expert correctly defi...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-07-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/20/14/4042 |
| _version_ | 1797561783412588544 |
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| author | Vinicius F. Vidal Leonardo M. Honório Felipe M. Dias Milena F. Pinto Alexandre L. Carvalho Andre L. M. Marcato |
| author_facet | Vinicius F. Vidal Leonardo M. Honório Felipe M. Dias Milena F. Pinto Alexandre L. Carvalho Andre L. M. Marcato |
| author_sort | Vinicius F. Vidal |
| collection | DOAJ |
| description | Thermal inspection is a powerful tool that enables the diagnosis of several components at its early stages. One critical aspect that influences thermal inspection outputs is the infrared reflection from external sources. This situation may change the readings, demanding that an expert correctly define the camera position, which is a time consuming and expensive operation. To mitigate this problem, this work proposes an autonomous system capable of identifying infrared reflections by filtering and fusing data obtained from both stereo and thermal cameras. The process starts by acquiring readings from multiples Observation Points (OPs) where, at each OP, the system processes the 3D point cloud and thermal image by fusing them together. The result is a dense point cloud where each point has its spatial position and temperature. Considering that each point’s information is acquired from multiple poses, it is possible to generate a temperature profile of each spatial point and filter undesirable readings caused by interference and other phenomena. To deploy and test this approach, a Directional Robotic System (DRS) is mounted over a traditional human-operated service vehicle. In that way, the DRS autonomously tracks and inspects any desirable equipment as the service vehicle passes them by. To demonstrate the results, this work presents the algorithm workflow, a proof of concept, and a real application result, showing improved performance in real-life conditions. |
| first_indexed | 2024-03-10T18:19:30Z |
| format | Article |
| id | doaj.art-0e039a82bd24453fafaf5f93586dcf46 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T18:19:30Z |
| publishDate | 2020-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-0e039a82bd24453fafaf5f93586dcf462023-11-20T07:23:45ZengMDPI AGSensors1424-82202020-07-012014404210.3390/s20144042Sensors Fusion and Multidimensional Point Cloud Analysis for Electrical Power System InspectionVinicius F. Vidal0Leonardo M. Honório1Felipe M. Dias2Milena F. Pinto3Alexandre L. Carvalho4Andre L. M. Marcato5Electrical Engineering Department, Federal University of Juiz de Fora, Juiz de Fora 36036, BrazilElectrical Engineering Department, Federal University of Juiz de Fora, Juiz de Fora 36036, BrazilElectrical Engineering Department, Federal University of Juiz de Fora, Juiz de Fora 36036, BrazilElectronics Department, Federal Center for Technological Education of Rio de Janeiro, Rio de Janeiro 20271, BrazilMRS Logistica, Juiz de Fora 36060, BrazilElectrical Engineering Department, Federal University of Juiz de Fora, Juiz de Fora 36036, BrazilThermal inspection is a powerful tool that enables the diagnosis of several components at its early stages. One critical aspect that influences thermal inspection outputs is the infrared reflection from external sources. This situation may change the readings, demanding that an expert correctly define the camera position, which is a time consuming and expensive operation. To mitigate this problem, this work proposes an autonomous system capable of identifying infrared reflections by filtering and fusing data obtained from both stereo and thermal cameras. The process starts by acquiring readings from multiples Observation Points (OPs) where, at each OP, the system processes the 3D point cloud and thermal image by fusing them together. The result is a dense point cloud where each point has its spatial position and temperature. Considering that each point’s information is acquired from multiple poses, it is possible to generate a temperature profile of each spatial point and filter undesirable readings caused by interference and other phenomena. To deploy and test this approach, a Directional Robotic System (DRS) is mounted over a traditional human-operated service vehicle. In that way, the DRS autonomously tracks and inspects any desirable equipment as the service vehicle passes them by. To demonstrate the results, this work presents the algorithm workflow, a proof of concept, and a real application result, showing improved performance in real-life conditions.https://www.mdpi.com/1424-8220/20/14/4042thermal inspectionmultidimensional point cloudsensor fusionautonomous inspectioninfrared noise filtering |
| spellingShingle | Vinicius F. Vidal Leonardo M. Honório Felipe M. Dias Milena F. Pinto Alexandre L. Carvalho Andre L. M. Marcato Sensors Fusion and Multidimensional Point Cloud Analysis for Electrical Power System Inspection Sensors thermal inspection multidimensional point cloud sensor fusion autonomous inspection infrared noise filtering |
| title | Sensors Fusion and Multidimensional Point Cloud Analysis for Electrical Power System Inspection |
| title_full | Sensors Fusion and Multidimensional Point Cloud Analysis for Electrical Power System Inspection |
| title_fullStr | Sensors Fusion and Multidimensional Point Cloud Analysis for Electrical Power System Inspection |
| title_full_unstemmed | Sensors Fusion and Multidimensional Point Cloud Analysis for Electrical Power System Inspection |
| title_short | Sensors Fusion and Multidimensional Point Cloud Analysis for Electrical Power System Inspection |
| title_sort | sensors fusion and multidimensional point cloud analysis for electrical power system inspection |
| topic | thermal inspection multidimensional point cloud sensor fusion autonomous inspection infrared noise filtering |
| url | https://www.mdpi.com/1424-8220/20/14/4042 |
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