Discrimination between strain and temperature effects using first and second-order diffraction from a long-period grating

Allsop, Thomas D.P.; Zhang, Lin; Webb, David J.; Bennion, I.

doi:10.1016/s0030-4018(02)01906-5

Cited by 18 publications

(9 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The LPFG sensor has the advantages of having a small size, low weight, anti-electromagnetic interference, high durability, and resistance to corrosion. LPFG sensors are sensitive to many environmental factors, such as temperature, 7 strain, 8 bending, 9 and refractive index. 10,11 The working principle of a LPFG sensor is based on the shift of the resonance wavelength that occurs when the LPFG is subjected to external physical parameters such as strain.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A fuzzy neural network model to determine axial strain measured by a long-period fiber grating sensor

Shen

et al. 2020

Measurement and Control

View full text Add to dashboard Cite

The paper reports an adaptive-network-based fuzzy inference system for the measurement of axial strain using long-period fiber grating sensors. The long-period fiber grating sensor supports optical resonances, which are sensitive to the change of axial strain. The axial strain can be quantified based on the wavelength shift and amplitude changes of the optical resonance. To improve the accuracy of axial strain quantification, this paper proposes the adaptive-network-based fuzzy inference system model. The adaptive-network-based fuzzy inference system model is trained using the strain data measured with long-period fiber grating sensors. The parameters of the membership functions used in the adaptive-network-based fuzzy inference system are set adaptively. In the adaptive-network-based fuzzy inference system–based method, the maximum relative error was found to be 1.5%, which is about one-ninth of that when the data fitting method was used. The R-squared statistics using the adaptive-network-based fuzzy inference system model is 0.9872, while that using the linear fitting algorithm is 0.8815. Compared with the conventional data fitting methods, the proposed approach is highly adaptive and versatile with the capability of improving the accuracy of strain quantification.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A fuzzy neural network model to determine axial strain measured by a long-period fiber grating sensor

Shen

et al. 2020

Measurement and Control

View full text Add to dashboard Cite

show abstract

“…The sensitivities at cryogenic temperatures down to 77 K were also studied. We also successfully discriminated the temperature and strain using a single LPG at a strain of up to 4000 m" using the negative temperature sensitivities and positive strain sensitivities 15) of two different loss peaks. This combination should decrease the amount of wavelength shift, particularly when the strain is large.…”

Section: Introductionmentioning

confidence: 99%

Long-Period Fiber-Grating Temperature Sensors in Ge–B-Codoped Fibers with Temperature/Strain Discrimination

Mizunami

Ishida

Yoshikura

2008

jjap

View full text Add to dashboard Cite

The temperature-sensing characteristics of long-period fiber gratings were compared for different Ge-B-codoped fibers. The temperature sensitivities varied by one order of magnitude depending on the composition of the core. The temperature sensitivity varied from 0.05 to 0.5 nm/K for the same grating period of 212 mm. The sensitivities at cryogenic temperatures down to 77 K were also studied. The strain sensitivities were measured at above the room temperature. Two different loss peaks of a single long-period grating with negative temperature sensitivities and positive strain sensitivities were used to distinguish applied temperature and strain. Discrimination characteristics using a Ge-B-codoped fiber were studied for a strain of up to 4000 m".

show abstract

“…Currently, the most promising candidates are fiber optic sensors, which can be embedded in composite materials to locally measure strain, both during cure and during use (Measures, 1992;Hadjiprocopiou et al, 1996;Guemes and Menendez, 2002). In the last 10 years, researchers have been working on the principles and techniques to measure strain (e.g., by using fiber Bragg gratings, or other types of sensors such as Fabry-Perot cavities), temperature or other parameters such as humidity (Ferreira et al, 2000;Chi et al, 2001;Allsop et al, 2002;Kronenberg et al, 2002;Lai et al, 2002;Rao et al, 2002;Sivanesan et al, 2002;Shu et al, 2002;Frazao et al, 2003;Han et al, 2003). Moreover, the process of fiber embedding was investigated to ensure reliability and precision of the measurement (Measures, 1992;Bao et al, 2002;Guemes and Menendez, 2002).…”

Section: Introductionmentioning

confidence: 99%

In situ Strain and Temperature Monitoring of Adaptive Composite Materials

Yoon

Costantini²,

Limberger³

et al. 2006

Journal of Intelligent Material Systems and Structures

View full text Add to dashboard Cite

An optical fiber sensor is designed to simultaneously measure strain and temperature in an adaptive composite material. The sensor is formed by splicing two fiber Bragg gratings (FBGs) close to each other, which are written in optical fibers with different core dopants and concentrations. Their temperature sensitivities are hence different. The sensor is tested on an adaptive composite laminate made of unidirectional Kevlar-epoxy prepreg plies. Several 150 mm diameter prestrained NiTiCu shape memory alloy (SMA) wires are embedded in the composite laminate together with one fiber sensor. Simultaneous monitoring of strain and temperature during the curing process and activation in an oven is demonstrated.

show abstract

Discrimination between strain and temperature effects using first and second-order diffraction from a long-period grating

Cited by 18 publications

References 14 publications

A fuzzy neural network model to determine axial strain measured by a long-period fiber grating sensor

A fuzzy neural network model to determine axial strain measured by a long-period fiber grating sensor

Long-Period Fiber-Grating Temperature Sensors in Ge–B-Codoped Fibers with Temperature/Strain Discrimination

In situ Strain and Temperature Monitoring of Adaptive Composite Materials

Contact Info

Product

Resources

About