M. F., Hassan and Nor Syukriah, Khalid (2021) Sensitivity identification of low-frequency cantilever fibre bragg grating accelerometer. International Journal of Integrated Engineering, 13 (7). pp. 235-244. ISSN 2229-838X (Print); 2600-7916 (Online). (Published)
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Abstract
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.
| Item Type: | Article |
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| Additional Information: | Indexed by Scopus |
| Uncontrolled Keywords: | Fibre Bragg grating accelerometer; Sensitivity; Cantilever Euler-Bernoulli beam model; Cascade-forward backpropagation neural network |
| Subjects: | T Technology > TJ Mechanical engineering and machinery |
| Faculty/Division: | College of Engineering |
| Depositing User: | Dr. Mohd Firdaus Hassan |
| Date Deposited: | 18 Apr 2022 02:06 |
| Last Modified: | 18 Apr 2022 02:06 |
| URI: | http://umpir.ump.edu.my/id/eprint/32634 |
| Download Statistic: | View Download Statistics |
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