An optical fibre sensor for the measurement of pressure

Dakin, J.P.; Wade, C. A.; Withers, P. B.

doi:10.1080/01468038808219350

Cited by 9 publications

(1 citation statement)

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“…Thanks to the complexity of the sensor systems which exist today, different approaches have been used in classification of optical fiber sensors. It could range from the essentially straightforward quantities, such as those based on the functions [20][21][22][23][24][25][26][27][28][29][30][31][32], or fiber types [22,[33][34][35][36][37][38][39]; or through the use of more precise subdivisions which focus on the modulation techniques [7,8,[40][41][42][43], or even the sources [1,23,44,45] employed (see Table 2.1). Due to the rapid development of the equipments and the optical measurement techniques, those kinds of classification methods may not be comprehensive and representative.…”

Section: Overview Of Optical Fiber Sensorsmentioning

confidence: 99%

Cavity ring fiber chemical sensors

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In recent years, optical fiber chemical sensors are paid increasing attentions due to their electrical passivity, remote sensing potentials and miniaturization feasibility, relying on the specific properties of the optical fibers. Highly sensitive spectroscopic techniques are highly demanded for enhancing the sensitivities of the optical fiber chemical sensors. This thesis mainly reports optical fiber cavity ring-down (CRD) and cavity ring-up (CRU) based-sensors for chemical sensing and other applications. The thesis begins with a comprehensive survey on the existing types and spectroscopic techniques for optical fiber chemical sensors. Following that, direct absorption spectroscopy, which is the most commonly used spectroscopic technique, is studied. The direct absorption spectroscopy-based optical fiber chemical sensors are analyzed• based on a practical setup. Aiming at reducing the effects of the main noises in this kind of sensor, a digital finite impulse response (FIR) filter is designed to improve this chemical sensor's sensitivity. The simulation and experimental results have shown that the detection accuracy is enhanced by this digital signal processing (DSP) technique. The success of introducing DSP technique into the sensing system motivates us to solve the challenge of identification of the unknown chemical mixture detected by an optical fiber chemical sensor using pattern recognition technique. An automatic chemical mixture recognition system by using support vector regression (SVR) technique is proposed. Using the recognition model, the chemical components and the corresponding concentrations could be identified automatically and a high measurement accuracy in terms of concentration is achieved.

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