{"title":"基于分数阶循环位移和无损相位展开的OFDR高精度分布式应变传感","authors":"Chenhuan Wang;Suozhen Zheng;Ji Liu;Jinhui Wu;Yaoyu Cheng;Haojin Yang;Peng Sun;Boyang Zhang","doi":"10.1109/JSEN.2025.3560710","DOIUrl":null,"url":null,"abstract":"The relative phase method in strain demodulation based on optical frequency domain reflectometry (OFDR) has received much attention for its high accuracy and spatial resolution, but has the unique phase wrapping problem with different proposed solutions. Some relative phase information is lost in these solutions, and the complex algorithms make it difficult to implement in devices. This article proposes a fractional cyclic shift method that matches any slope, which provides a nondestructive solution to phase wrapping and is easy to implement. Additionally, this article presents the theory of cyclic shifts with zero padding, conversion formulae for fractional shift points, and the mathematical relationship between relative phase slope and optical frequency domain shift under the same strain. Using fractional cyclic shift and peak finding method based on variance statistic, this article accurately determines the start and end positions of the strain, providing basic strain phase information. Finally, the method achieves the strain measurement with spatial resolution of 0.907 mm, distance of 36.7 m, strain accuracy of <inline-formula> <tex-math>$0.5~\\mu \\varepsilon $ </tex-math></inline-formula>, maximum error of <inline-formula> <tex-math>$0.2~\\mu \\varepsilon $ </tex-math></inline-formula>, and standard deviation of <inline-formula> <tex-math>$0.077~\\mu \\varepsilon $ </tex-math></inline-formula> through bidirectional Chebyshev low-pass filtering, providing low-error strain demodulation with sub-millimeter resolution in the medium distance. This article also compares different filtering methods finding that bidirectional Chebyshev filtering avoids phase distortion and has one-third the error of finite impulse response (FIR) filtering with different windows.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 11","pages":"19290-19301"},"PeriodicalIF":4.3000,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"High Accuracy Distributed Strain Sensing Based on Fractional Cyclic Shift With Nondestructive Phase Unwrapping in OFDR\",\"authors\":\"Chenhuan Wang;Suozhen Zheng;Ji Liu;Jinhui Wu;Yaoyu Cheng;Haojin Yang;Peng Sun;Boyang Zhang\",\"doi\":\"10.1109/JSEN.2025.3560710\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The relative phase method in strain demodulation based on optical frequency domain reflectometry (OFDR) has received much attention for its high accuracy and spatial resolution, but has the unique phase wrapping problem with different proposed solutions. Some relative phase information is lost in these solutions, and the complex algorithms make it difficult to implement in devices. This article proposes a fractional cyclic shift method that matches any slope, which provides a nondestructive solution to phase wrapping and is easy to implement. Additionally, this article presents the theory of cyclic shifts with zero padding, conversion formulae for fractional shift points, and the mathematical relationship between relative phase slope and optical frequency domain shift under the same strain. Using fractional cyclic shift and peak finding method based on variance statistic, this article accurately determines the start and end positions of the strain, providing basic strain phase information. Finally, the method achieves the strain measurement with spatial resolution of 0.907 mm, distance of 36.7 m, strain accuracy of <inline-formula> <tex-math>$0.5~\\\\mu \\\\varepsilon $ </tex-math></inline-formula>, maximum error of <inline-formula> <tex-math>$0.2~\\\\mu \\\\varepsilon $ </tex-math></inline-formula>, and standard deviation of <inline-formula> <tex-math>$0.077~\\\\mu \\\\varepsilon $ </tex-math></inline-formula> through bidirectional Chebyshev low-pass filtering, providing low-error strain demodulation with sub-millimeter resolution in the medium distance. This article also compares different filtering methods finding that bidirectional Chebyshev filtering avoids phase distortion and has one-third the error of finite impulse response (FIR) filtering with different windows.\",\"PeriodicalId\":447,\"journal\":{\"name\":\"IEEE Sensors Journal\",\"volume\":\"25 11\",\"pages\":\"19290-19301\"},\"PeriodicalIF\":4.3000,\"publicationDate\":\"2025-04-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Sensors Journal\",\"FirstCategoryId\":\"103\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10971906/\",\"RegionNum\":2,\"RegionCategory\":\"综合性期刊\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Sensors Journal","FirstCategoryId":"103","ListUrlMain":"https://ieeexplore.ieee.org/document/10971906/","RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
High Accuracy Distributed Strain Sensing Based on Fractional Cyclic Shift With Nondestructive Phase Unwrapping in OFDR
The relative phase method in strain demodulation based on optical frequency domain reflectometry (OFDR) has received much attention for its high accuracy and spatial resolution, but has the unique phase wrapping problem with different proposed solutions. Some relative phase information is lost in these solutions, and the complex algorithms make it difficult to implement in devices. This article proposes a fractional cyclic shift method that matches any slope, which provides a nondestructive solution to phase wrapping and is easy to implement. Additionally, this article presents the theory of cyclic shifts with zero padding, conversion formulae for fractional shift points, and the mathematical relationship between relative phase slope and optical frequency domain shift under the same strain. Using fractional cyclic shift and peak finding method based on variance statistic, this article accurately determines the start and end positions of the strain, providing basic strain phase information. Finally, the method achieves the strain measurement with spatial resolution of 0.907 mm, distance of 36.7 m, strain accuracy of $0.5~\mu \varepsilon $ , maximum error of $0.2~\mu \varepsilon $ , and standard deviation of $0.077~\mu \varepsilon $ through bidirectional Chebyshev low-pass filtering, providing low-error strain demodulation with sub-millimeter resolution in the medium distance. This article also compares different filtering methods finding that bidirectional Chebyshev filtering avoids phase distortion and has one-third the error of finite impulse response (FIR) filtering with different windows.
期刊介绍:
The fields of interest of the IEEE Sensors Journal are the theory, design , fabrication, manufacturing and applications of devices for sensing and transducing physical, chemical and biological phenomena, with emphasis on the electronics and physics aspect of sensors and integrated sensors-actuators. IEEE Sensors Journal deals with the following:
-Sensor Phenomenology, Modelling, and Evaluation
-Sensor Materials, Processing, and Fabrication
-Chemical and Gas Sensors
-Microfluidics and Biosensors
-Optical Sensors
-Physical Sensors: Temperature, Mechanical, Magnetic, and others
-Acoustic and Ultrasonic Sensors
-Sensor Packaging
-Sensor Networks
-Sensor Applications
-Sensor Systems: Signals, Processing, and Interfaces
-Actuators and Sensor Power Systems
-Sensor Signal Processing for high precision and stability (amplification, filtering, linearization, modulation/demodulation) and under harsh conditions (EMC, radiation, humidity, temperature); energy consumption/harvesting
-Sensor Data Processing (soft computing with sensor data, e.g., pattern recognition, machine learning, evolutionary computation; sensor data fusion, processing of wave e.g., electromagnetic and acoustic; and non-wave, e.g., chemical, gravity, particle, thermal, radiative and non-radiative sensor data, detection, estimation and classification based on sensor data)
-Sensors in Industrial Practice