&lt;title&gt;Design, fabrication, and evaluation of an optical fiber sensor for tensile and compressive strain measurements via the use of white light interferometry&lt;/title&gt;

Liu, Tonguy; Brooks, David J.; Martín, Anthony R.; Badcock, Rodney A.; Fernando, Gerard Franklyn

doi:10.1117/12.240880

Cited by 14 publications

(15 citation statements)

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“…(15)]. The above calculation was confirmed by computer simulation in which the variance and covariance terms in Eq.…”

Section: Appendix A: Noise Reduction Mechanism Of Type II Estimationssupporting

confidence: 57%

“…An alternative TYPE I approach relies on Φ OPD · k, where multiple Φ and k values are obtained from the spectrogram, and consequently calculation of OPD is converted to frequency estimation by linear regression (LR). A simple but popular version uses only two points and usually employs peaks, valleys, or zero-crossing points [1,3,15]. This method offers low resolution and works better for situations where only a few peaks exist in the spectrum.…”

Section: Introductionmentioning

confidence: 99%

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Signal processing of white-light interferometric low-finesse fiber-optic Fabry–Perot sensors

Wang

2013

Appl. Opt.

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Signal processing for low-finesse fiber-optic Fabry-Perot sensors based on white-light interferometry is investigated. The problem is demonstrated as analogous to the parameter estimation of a noisy, real, discrete harmonic of finite length. The Cramer-Rao bounds for the estimators are given, and three algorithms are evaluated and proven to approach the bounds. A long-standing problem with these types of sensors is the unpredictable jumps in the phase estimation. Emphasis is made on the property and mechanism of the "total phase" estimator in reducing the estimation error, and a varying phase term in the total phase is identified to be responsible for the unwanted demodulation jumps. The theories are verified by simulation and experiment. A solution to reducing the probability of jump is demonstrated.

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“…(15)]. The above calculation was confirmed by computer simulation in which the variance and covariance terms in Eq.…”

Section: Appendix A: Noise Reduction Mechanism Of Type II Estimationssupporting

confidence: 57%

Section: Introductionmentioning

confidence: 99%

Signal processing of white-light interferometric low-finesse fiber-optic Fabry–Perot sensors

Wang

2013

Appl. Opt.

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“…2. It consists of a superluminescent diode (Superlum Broadlighter S1300-G-I-20, Gaussian spectral density, λ MAX = 1290 nm, ∆λ FWHM = 50 nm) acting as a light source, connected to the FabryPerot interferometer by a single--mode SMF-28 ber, and an Ando AQ6319 optical spectrum analyzer with resolution bandwidth set to 1 nm working as the detection setup [6]. Acquired spectra are processed by a computer connected to the spectrum analyzer.…”

Section: Methodsmentioning

confidence: 99%

Optimization of a Fabry-Perot Sensing Interferometer Design for an Optical Fiber Sensor of Hematocrit Level

Wierzba

Jędrzejewska‐Szczerska

2013

Acta Phys. Pol. A

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Continuous measurement of the hematocrit level in blood can potentially be performed using optical bre sensors. The FabryPerot interferometric sensors are a promising candidate in this application. The most important step in the design of the sensor is design of the sensing interferometer. Adequate cavity length and high interference contrast are two most important requirements in this application. The design method of the sensing interferometer presented in this paper uses a Gaussian beam approximation. In order to verify the design, an optical bre Fabry Perot interferometer with adjustable cavity length was built. Its performance was tested, conrming validity of the design approach.

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“…For example, fibre-optic sensor systems have been developed to enable the following measurands to be accessed in real-time in composites: displacement [ 33 ], strain and temperature [ 34 ], vibration [ 35 ], pressure [ 36 , 37 ], acoustic emission [ 38 ], refractive index [ 39 ], quantitative data on the relative concentrations of specified chemical species during cross-linking [ 40 ], diffusion of moisture [ 41 ], fatigue [ 42 ] and impact damage [ 43 ].…”

Section: Introductionmentioning

confidence: 99%

Self-Sensing Composites: In-Situ Detection of Fibre Fracture

Malik

Wang

Curtis

et al. 2016

Sensors

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The primary load-bearing component in a composite material is the reinforcing fibres. This paper reports on a technique to study the fracture of individual reinforcing fibres or filaments in real-time. Custom-made small-diameter optical fibres with a diameter of 12 (±2) micrometres were used to detect the fracture of individual filaments during tensile loading of unreinforced bundles and composites. The unimpregnated bundles were end-tabbed and tensile tested to failure. A simple technique based on resin-infusion was developed to manufacture composites with a negligible void content. In both cases, optical fibre connectors were attached to the ends of the small-diameter optical fibre bundles to enable light to be coupled into the bundle via one end whilst the opposite end was photographed using a high-speed camera. The feasibility of detecting the fracture of each of the filaments in the bundle and composite was demonstrated. The in-situ damage detection technique was also applied to E-glass bundles and composites; this will be reported in a subsequent publication.

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<title>Design, fabrication, and evaluation of an optical fiber sensor for tensile and compressive strain measurements via the use of white light interferometry</title>

Cited by 14 publications

References 0 publications

Signal processing of white-light interferometric low-finesse fiber-optic Fabry–Perot sensors

Signal processing of white-light interferometric low-finesse fiber-optic Fabry–Perot sensors

Optimization of a Fabry-Perot Sensing Interferometer Design for an Optical Fiber Sensor of Hematocrit Level

Self-Sensing Composites: In-Situ Detection of Fibre Fracture

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