Deep Learning-Based Multimode Fiber Distributed Temperature Sensing
As a laser beam passes through a multimode fiber (MMF), a speckle pattern is generated, which is sensitive to temperature, thereby making the MMF a temperature-sensing element. A deep learning technique is employed to the MMF-based temperature sensor, to obtain high-precision temperature sensing. We...
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| Language: | English |
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MDPI AG
2025-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/9/2811 |
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| author | Luxuan Yang Xiaoyan Wang Tong Wu Huichuan Lin Songjie Luo Ziyang Chen Yongxin Liu Jixiong Pu |
| author_facet | Luxuan Yang Xiaoyan Wang Tong Wu Huichuan Lin Songjie Luo Ziyang Chen Yongxin Liu Jixiong Pu |
| author_sort | Luxuan Yang |
| collection | DOAJ |
| description | As a laser beam passes through a multimode fiber (MMF), a speckle pattern is generated, which is sensitive to temperature, thereby making the MMF a temperature-sensing element. A deep learning technique is employed to the MMF-based temperature sensor, to obtain high-precision temperature sensing. We designed an MMF-based temperature-sensing configuration and developed a dual-output Convolutional Neural Network (CNN) for predicting both the temperature and the position of the heating point, and we constructed a dataset. It was shown that the location prediction accuracy reached 100%, while the temperature prediction accuracy (within a ±1 °C error margin) was 100% and 95.12% in the two experiments, respectively. The precision of the predicting heating point was less than 1 cm. Different types of MMFs were used in temperature measurements, showing that the accuracy remained quite high. This non-contact, high-precision MMF-based temperature measurement method, driven by deep learning, is suitable for applications in hazardous environments. |
| format | Article |
| id | doaj-art-65dbdb6b92e3460baceabfa516baeb85 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-65dbdb6b92e3460baceabfa516baeb852025-08-20T02:59:08ZengMDPI AGSensors1424-82202025-04-01259281110.3390/s25092811Deep Learning-Based Multimode Fiber Distributed Temperature SensingLuxuan Yang0Xiaoyan Wang1Tong Wu2Huichuan Lin3Songjie Luo4Ziyang Chen5Yongxin Liu6Jixiong Pu7Fujian Provincial Key Laboratory of Light Propagation and Transformation, College of Information Science & Engineering, Huaqiao University, Xiamen 361021, ChinaFujian Provincial Key Laboratory of Light Propagation and Transformation, College of Information Science & Engineering, Huaqiao University, Xiamen 361021, ChinaFujian Provincial Key Laboratory of Light Propagation and Transformation, College of Information Science & Engineering, Huaqiao University, Xiamen 361021, ChinaCollege of Physics and Information Engineering, Minnan Normal University, Zhangzhou 363000, ChinaFujian Provincial Key Laboratory of Light Propagation and Transformation, College of Information Science & Engineering, Huaqiao University, Xiamen 361021, ChinaFujian Provincial Key Laboratory of Light Propagation and Transformation, College of Information Science & Engineering, Huaqiao University, Xiamen 361021, ChinaFujian Provincial Key Laboratory of Light Propagation and Transformation, College of Information Science & Engineering, Huaqiao University, Xiamen 361021, ChinaFujian Provincial Key Laboratory of Light Propagation and Transformation, College of Information Science & Engineering, Huaqiao University, Xiamen 361021, ChinaAs a laser beam passes through a multimode fiber (MMF), a speckle pattern is generated, which is sensitive to temperature, thereby making the MMF a temperature-sensing element. A deep learning technique is employed to the MMF-based temperature sensor, to obtain high-precision temperature sensing. We designed an MMF-based temperature-sensing configuration and developed a dual-output Convolutional Neural Network (CNN) for predicting both the temperature and the position of the heating point, and we constructed a dataset. It was shown that the location prediction accuracy reached 100%, while the temperature prediction accuracy (within a ±1 °C error margin) was 100% and 95.12% in the two experiments, respectively. The precision of the predicting heating point was less than 1 cm. Different types of MMFs were used in temperature measurements, showing that the accuracy remained quite high. This non-contact, high-precision MMF-based temperature measurement method, driven by deep learning, is suitable for applications in hazardous environments.https://www.mdpi.com/1424-8220/25/9/2811convolutional neural networksmultimode fiberstemperature predictionposition predictionspeckle imagingdistributed sensing |
| spellingShingle | Luxuan Yang Xiaoyan Wang Tong Wu Huichuan Lin Songjie Luo Ziyang Chen Yongxin Liu Jixiong Pu Deep Learning-Based Multimode Fiber Distributed Temperature Sensing Sensors convolutional neural networks multimode fibers temperature prediction position prediction speckle imaging distributed sensing |
| title | Deep Learning-Based Multimode Fiber Distributed Temperature Sensing |
| title_full | Deep Learning-Based Multimode Fiber Distributed Temperature Sensing |
| title_fullStr | Deep Learning-Based Multimode Fiber Distributed Temperature Sensing |
| title_full_unstemmed | Deep Learning-Based Multimode Fiber Distributed Temperature Sensing |
| title_short | Deep Learning-Based Multimode Fiber Distributed Temperature Sensing |
| title_sort | deep learning based multimode fiber distributed temperature sensing |
| topic | convolutional neural networks multimode fibers temperature prediction position prediction speckle imaging distributed sensing |
| url | https://www.mdpi.com/1424-8220/25/9/2811 |
| work_keys_str_mv | AT luxuanyang deeplearningbasedmultimodefiberdistributedtemperaturesensing AT xiaoyanwang deeplearningbasedmultimodefiberdistributedtemperaturesensing AT tongwu deeplearningbasedmultimodefiberdistributedtemperaturesensing AT huichuanlin deeplearningbasedmultimodefiberdistributedtemperaturesensing AT songjieluo deeplearningbasedmultimodefiberdistributedtemperaturesensing AT ziyangchen deeplearningbasedmultimodefiberdistributedtemperaturesensing AT yongxinliu deeplearningbasedmultimodefiberdistributedtemperaturesensing AT jixiongpu deeplearningbasedmultimodefiberdistributedtemperaturesensing |