Enhancing space sensor resilience with transfer learning in data-scarce scenarios
In Mars exploration missions, harsh environmental conditions, such as those generated by dust devils, can damage sensing systems. Soft sensors offer a promising solution in such scenarios, but their implementation is challenging when data is scarce. This paper explores the use of transfer learning (...
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| Format: | Article |
| Language: | English |
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IOP Publishing
2025-01-01
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| Series: | Machine Learning: Science and Technology |
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| Online Access: | https://doi.org/10.1088/2632-2153/adfd38 |
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| author | Dileep Kumar Manuel Domínguez-Pumar Beatriz Otero-Calviño Joan Pons-Nin Josefina Torres Mercedes Marín Javier Gómez-Elvira Luis Mora Sara Navarro Jose Rodríguez-Manfredi |
| author_facet | Dileep Kumar Manuel Domínguez-Pumar Beatriz Otero-Calviño Joan Pons-Nin Josefina Torres Mercedes Marín Javier Gómez-Elvira Luis Mora Sara Navarro Jose Rodríguez-Manfredi |
| author_sort | Dileep Kumar |
| collection | DOAJ |
| description | In Mars exploration missions, harsh environmental conditions, such as those generated by dust devils, can damage sensing systems. Soft sensors offer a promising solution in such scenarios, but their implementation is challenging when data is scarce. This paper explores the use of transfer learning (TL) to enhance sensor resilience, specifically addressing the issue of limited data availability. First, pre-trained models are developed using wind sensor data from the Temperature and Wind Sensors for the TWINS instrument (NASA InSight Mission), serving as the source domain for this study. These models account for the malfunction of a single wind sensing board or transducer. The pre-trained models are then adapted using TL to the Mars Environmental Dynamics Analyzer (MEDA) wind sensor (NASA Perseverance Mission), which shares common sensing principles, and which suffered a malfunction during the mission. Hyperparameter tuning further improves the performance of the TL models, yielding better results than models trained solely on a small MEDA dataset. The results demonstrate the effectiveness of the TL-based approach in recovering variables from the MEDA wind sensor despite partial failures and data limitations. Overall, the TL-based method improves performance by 10.21%–22.04% compared to models trained exclusively on the limited MEDA dataset. |
| format | Article |
| id | doaj-art-96bdfb96dbad47bd8c909f18e7ac72ec |
| institution | Kabale University |
| issn | 2632-2153 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Machine Learning: Science and Technology |
| spelling | doaj-art-96bdfb96dbad47bd8c909f18e7ac72ec2025-08-26T07:16:50ZengIOP PublishingMachine Learning: Science and Technology2632-21532025-01-016303LT0110.1088/2632-2153/adfd38Enhancing space sensor resilience with transfer learning in data-scarce scenariosDileep Kumar0https://orcid.org/0000-0002-6211-1078Manuel Domínguez-Pumar1https://orcid.org/0000-0001-5439-7953Beatriz Otero-Calviño2https://orcid.org/0000-0002-9194-559XJoan Pons-Nin3https://orcid.org/0000-0002-0356-5678Josefina Torres4https://orcid.org/0000-0003-1035-6740Mercedes Marín5https://orcid.org/0000-0003-2328-1303Javier Gómez-Elvira6https://orcid.org/0000-0002-9068-9846Luis Mora7https://orcid.org/0000-0002-8209-1190Sara Navarro8https://orcid.org/0000-0001-8606-7799Jose Rodríguez-Manfredi9https://orcid.org/0000-0003-0461-9815Universitat Politècnica de Catalunya (UPC) , Barcelona, SpainUniversitat Politècnica de Catalunya (UPC) , Barcelona, SpainDepartment of Computer Architecture, Universitat Politècnica de Catalunya (UPC) , Barcelona, SpainUniversitat Politècnica de Catalunya (UPC) , Barcelona, SpainCentro de Astrobiología (INTA-CSIC) , Madrid, SpainCentro de Astrobiología (INTA-CSIC) , Madrid, SpainCentro de Astrobiología (INTA-CSIC) , Madrid, SpainCentro de Astrobiología (INTA-CSIC) , Madrid, SpainCentro de Astrobiología (INTA-CSIC) , Madrid, SpainCentro de Astrobiología (INTA-CSIC) , Madrid, SpainIn Mars exploration missions, harsh environmental conditions, such as those generated by dust devils, can damage sensing systems. Soft sensors offer a promising solution in such scenarios, but their implementation is challenging when data is scarce. This paper explores the use of transfer learning (TL) to enhance sensor resilience, specifically addressing the issue of limited data availability. First, pre-trained models are developed using wind sensor data from the Temperature and Wind Sensors for the TWINS instrument (NASA InSight Mission), serving as the source domain for this study. These models account for the malfunction of a single wind sensing board or transducer. The pre-trained models are then adapted using TL to the Mars Environmental Dynamics Analyzer (MEDA) wind sensor (NASA Perseverance Mission), which shares common sensing principles, and which suffered a malfunction during the mission. Hyperparameter tuning further improves the performance of the TL models, yielding better results than models trained solely on a small MEDA dataset. The results demonstrate the effectiveness of the TL-based approach in recovering variables from the MEDA wind sensor despite partial failures and data limitations. Overall, the TL-based method improves performance by 10.21%–22.04% compared to models trained exclusively on the limited MEDA dataset.https://doi.org/10.1088/2632-2153/adfd38Martian wind sensorssoft sensorsdeep learningtransfer learningsmall dataset |
| spellingShingle | Dileep Kumar Manuel Domínguez-Pumar Beatriz Otero-Calviño Joan Pons-Nin Josefina Torres Mercedes Marín Javier Gómez-Elvira Luis Mora Sara Navarro Jose Rodríguez-Manfredi Enhancing space sensor resilience with transfer learning in data-scarce scenarios Machine Learning: Science and Technology Martian wind sensors soft sensors deep learning transfer learning small dataset |
| title | Enhancing space sensor resilience with transfer learning in data-scarce scenarios |
| title_full | Enhancing space sensor resilience with transfer learning in data-scarce scenarios |
| title_fullStr | Enhancing space sensor resilience with transfer learning in data-scarce scenarios |
| title_full_unstemmed | Enhancing space sensor resilience with transfer learning in data-scarce scenarios |
| title_short | Enhancing space sensor resilience with transfer learning in data-scarce scenarios |
| title_sort | enhancing space sensor resilience with transfer learning in data scarce scenarios |
| topic | Martian wind sensors soft sensors deep learning transfer learning small dataset |
| url | https://doi.org/10.1088/2632-2153/adfd38 |
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