Sensitivity Identification of Low-frequency Cantilever Fibre Bragg Grating Accelerometer

Vibration response of low-frequency cantilever fibre Bragg grating (FBG) accelerometer produced by Euler–Bernoulli model (namely FBG-MM model) is found to be frequency-dependent, unsimilar to SDOF model. Therefore, the sensitivity of the cantilever FBG accelerometer could not be identified using polynomial or basic fitting methods. This paper presents the use of cascade-forward backpropagation neural network (CFB) to predict the sensitivity of the cantilever FBG accelerometer in a "black box", which refers to the behaviour of the deep neural network. The inputs of the network are maximum base accelerations and forcing frequencies, which was set between 20 and 90 Hz (below than the first fundamental frequency of the proposed FBG accelerometer), while the output is the wavelength shift. The validation results show that the wavelength shift predicted by the trained CFB demonstrates good agreement with the FBG-MM, with the input parameter within the range of that used in training process. In addition, results also show that the trained CFB would be invalid if the input parameter is out of the range of that used in training process. In real acceleration measurement, since the forcing frequency is unknown beforehand, the trained CFB must be re-trained by considering the maximum base accelerations are embedded with forcing frequencies

Sensitivity identification of low-frequency cantilever fibre bragg grating accelerometer

Vibration response of low-frequency cantilever fibre Bragg grating (FBG) accelerometer produced by Euler–Bernoulli model (namely FBG-MM model) is found to be frequency-dependent, unsimilar to SDOF model. Therefore, the sensitivity of the cantilever FBG accelerometer could not be identified using polynomial or basic fitting methods. This paper presents the use of cascade-forward backpropagation neural network (CFB) to predict the sensitivity of the cantilever FBG accelerometer in a "black box", which refers to the behaviour of the deep neural network. The inputs of the network are maximum base accelerations and forcing frequencies, which was set between 20 and 90 Hz (below than the first fundamental frequency of the proposed FBG accelerometer), while the output is the wavelength shift. The validation results show that the wavelength shift predicted by the trained CFB demonstrates good agreement with the FBG-MM, with the input parameter within the range of that used in training process. In addition, results also show that the trained CFB would be invalid if the input parameter is out of the range of that used in training process. In real acceleration measurement, since the forcing frequency is unknown beforehand, the trained CFB must be re-trained by considering the maximum base accelerations are embedded with forcing frequencies

Design and Modeling of a High Sensitivity Fiber Bragg Grating-Based Accelerometer

Use of a detailed theoretical model has allowed the optimization of the design of a high sensitivity accelerometer, based on a fiber Bragg grating (FBG) and an accelerometer based on this design has been demonstrated experimentally. With a universal model based on double-point encapsulation established, the performance of the device in terms of its optimal sensitivity and frequency distribution has been analyzed, with an optimization `figure of merit' using the product of the sensitivity and the resonant frequency being presented. The experimental results obtained indicate that the FBG-based accelerometer thus developed shows a broad, flat frequency band, a corresponding flat range sensitivity of ~ 152.0pm/G, a resonant frequency of 441.0Hz, and a cross-axis sensitivity of less than 3.6% of the main-axis sensitivity. An accelerometer of this type and with this performance thus has the potential for the important field of low frequency oil-gas seismic exploration

Investigation of low frequency fibre Bragg grating accelerometer based on thermoplastic cantilever beam

Vibration measurement technique is very important in structural integrity monitoring. Various fibre Bragg grating (FBG) based accelerometers have been developed for vibration measurement. However, most of the researchers focused on high frequency monitoring and only few reported works are based on low frequency measurement. Therefore, this paper presented a low frequency FBG accelerometer based on Polyphenylene Ether (PPE) thermoplastic cantilever beam. The proposed FBG accelerometer was attached to a shaker and vibration signals were given with variations in frequency and acceleration. As a result, the FBG accelerometer has a sensitivity of 110 pm/g and natural frequency of 9 Hz. The proposed accelerometer capable to detect low frequency of 2 Hz at 0.04 g which is suitable for utilisation in seismic monitoring of earthquake

Development of a non-inertia mass fibre bragg grating accelerometer based on a single diaphragm mechanism and its vibration response analysis

The development of the fibre Bragg grating (FBG) sensor as an accelerometer has received considerable attention since the FBG sensor is remarkably sensitive to strain. The inclusion of inertia mass in the diaphragm-type FBG accelerometer increased the complexity of the accelerometer mechanism. Moreover, numerical and experimental studies are not comprehensively reported and published, despite the fact that several accelerometer aspects should be thoroughly investigated. The overall aim of this thesis is to present a new design of a small and fabricable, diaphragm-type non-inertia mass FBG accelerometer (FBGA-SD) that comes with new features, as well as its comprehensive numerical and experimental investigation. This research begins with the development of five FBGA-SD designs and their concept scoring. The dynamic of the final FBGA-SD design is then investigated using finite element modal analysis followed by harmonic response analysis to determine the location of maximum strain on the diaphragm to place the FBG sensor. The functionality of the FBGA-SD is finally investigated through transient response analysis and experimental work as well as sensitivity determination. The final design of FBGA-SD with dimensions of 16 mm × 16 mm × 10 mm and a weight of 4 grammes has eliminated the weaknesses of the previous four FBGA-SD designs, with new features introduced particularly in the lengthening of the FBG tunnel and the invention of a through-hole for monitoring the FBG sensor inside the diaphragm pocket. Finite element modal analysis has ensured that the first natural frequency of the diaphragm is low (13, 380 Hz) and far from that of the housing (20, 689 Hz) in order to avoid the dynamic of the housing affecting accelerometer response. The location of the maximum strain for placing the FBG sensor on the diaphragm is determined, with the two best positions found to be in the middle and along the edges of the diaphragm. Due to the fact that the edge of the diaphragm is a clamped area, positioning the FBG sensor in its middle would be ideal. The response of the wavelength shift obtained from transient response analysis and experiment agrees well in terms of pattern and phase but differs by 50% of amplitude. It should also be mentioned that the base acceleration and the wavelength shift both demonstrate that they are in the same phase with one another. The 50% difference in amplitude of the wavelength shift reflects the sensitivity of the FBGA-SD, where the experimental sensitivity is 9.64×10-5 nm/g and the transient response analysis gives 4.79×10-5 nm/g, valid for the range of excitation frequencies of 10 to 147 Hz and maximum base acceleration of 10.5 m/s2. Within these ranges, the sensitivity is not frequency dependent

Sensitivity identification of low-frequency cantilever fibre bragg grating accelerometer using cascade-forward backpropagation neural network

The frequency-dependent issues and instrumentation requirement for FBG sensors necessitate the identification of the sensitivity of the cantilever FBG accelerometer using machine learning. As result, this article presents a cascade-forward backpropagation (CFB) neural network with an orthogonally-phase chirp signal with a range of constant forcing frequency and steadily increasing base acceleration amplitude as its input. This input/output data set was numerically calculated by integrating modal model and Euler-Bernoulli beam approach (FBG-MM). The maximum amplitude of the base acceleration was 200 m/s2 and the forcing frequencies and location of the FBG sensor mounted on the beam measured from the fixed end were 1 to 90 Hz and 0.03 m, respectively. The trained CFB predicted the wavelength shift very well, but it was restricted to one-half of the forcing frequencies of those used in the CFB training process, whereas the base acceleration is not an important element in determining the sensitivity of the FBG accelerometer. In terms of the FBG sensor’s location on the beam, considering a few positions will greatly expand the CFB’s capabilities. Future work will include the use of the trained CFB as “black-box sensitivity” for actual acceleration measurement, as well as the use of empirical data to replace the numerical FBG-MM as the input/output training data set

Highly Sensitive Multicore Fiber Accelerometer for Low Frequency Vibration Sensing

We report on a compact, highly sensitive all-fiber accelerometer suitable for low frequency and low amplitude vibration sensing. The sensing elements in the device are two short segments of strongly coupled asymmetric multicore fiber (MCF) fusion spliced at 180 degrees with respect to each other. Such segments of MCF are sandwiched between standard single mode fibers. The reflection spectrum of the device exhibits a narrow spectrum whose height and position in wavelength changes when it is subjected to vibrations. The interrogation of the accelerometer was carried out by a spectrometer and a photodetector to measure simultaneously wavelength shift and light power variations. The device was subjected to a wide range of vibration frequencies, from 1 mHz to 30 Hz, and accelerations from 0.76 mg to 29.64 mg, and performed linearly, with a sensitivity of 2.213 nW/mg. Therefore, we believe the accelerometer reported here may represent an alternative to existing electronic and optical accelerometers, especially for low frequency and amplitude vibrations, thanks to its compactness, simplicity, cost-effectiveness, implementation easiness and high sensitivity.Ministerio de Economia y Competitividad; Ministerio de Ciencia, Innovacion y Universidades; European Regional Development Fund (PGC2018-101997-B-I00 and RTI2018-094669-B-C31); Gobierno Vasco/Eusko Jaurlaritza (IT933-16); ELKARTEK KK-2019/00101 (mu 4Indust) and ELKARTEK KK-2019/00051 (SMARTRESNAK). The work of Angel Ortega-Gomez is funded by a PhD fellowship from the Spain Government. The work of Josu Amorebieta is funded by a PhD fellowship from the University of the Basque Country UPV/EHU

Highly sensitive multicore fiber accelerometer for low frequency vibration sensing

We report on a compact, highly sensitive all-fiber accelerometer suitable for low frequency and low amplitude vibration sensing. The sensing elements in the device are two short segments of strongly coupled asymmetric multicore fiber (MCF) fusion spliced at 180{\deg} with respect to each other. Such segments of MCF are sandwiched between standard single mode fibers. The reflection spectrum of the device exhibits a narrow spectrum whose height and position in wavelength changes when it is subjected to vibrations. The interrogation of the accelerometer was carried out by a spectrometer and a photodetector to measure simultaneously wavelength shift and light power variations. The device was subjected to a wide range of vibration frequencies, from 1 mHz to 30 Hz, and accelerations from 0.76 mg to 29.64 mg, and performed linearly, with a sensitivity of 2.213 nW/mg. Therefore, we believe the accelerometer reported here may represent an alternative to existing electronic and optical accelerometers, especially for low frequency and amplitude vibrations, thanks to its compactness, simplicity, cost-effectiveness, implementation easiness and high sensitivity