Modelling of a cantilever fiber bragg grating accelerometer using integrated euler-bernoulli beam and modal models and its sensitivity identification / Nor Syukriah Binti Khalid

Faculty of Mechanical and Automotive Engineering Technology

Thesis (Doctor of Philosophy) -- Universiti Malaysia Pahang – 2025

Includes bibliographical references

As the FBG sensor relies on a host to function as an accelerometer, the characteristics of the host system significantly influence the response of sensor to acceleration. Therefore, the study of FBG accelerometers, acknowledged as opto-mechanical devices, is undertaken from a mechanical aspect. FBG accelerometers encounter significant challenges in achieving accurate acceleration measurements at low frequencies due to their frequency-dependent sensitivity and the limitations of conventional single-degree-of-freedom (SDOF) models. The main objective of this study is to reach new levels of accuracy and applicability by improving mathematical models, examining sensitivity variations through the integration of tip masses, and utilizing neural network techniques for sensitivity determination. This will address the challenges of enhancing performance and reliability in dynamic scenarios, leveraging advanced approaches such as machine learning. To address the limitations of the SDOF model, this study introduces an enhanced model that combines the Euler-Bernoulli beam theory with a modal-based approach. This advanced model aims to provide a highly accurate representation of FBG accelerometer behaviour, significantly improving upon the precision of the SDOF model. The investigation examines two configurations: FBG-MM (without tip mass) and FBG-MMTP (with tip mass), to analyse their dynamic responses. Additionally, the study identifies a frequency-dependent characteristic in the system, which is proposed to be addressed through the application of neural networks for predicting and optimizing system behaviour. There is no single qualitative comparison between FBG-MM and SDOF since different excitation frequencies yield varying percentage differences. For example, at an excitation frequency of 20 Hz, the peak difference is approximately 78%, while at 90 Hz, it is 135%. The difference between the two models is not the primary indicator of their effectiveness; rather, it is the accuracy of the Euler-Bernoulli model in capturing the dynamic behaviour that holds greater significance for this study. The FBG-MM exhibited credibility in validating against experimental wavelength shifts, but there is room for improvement in future iterations. It is recommended to incorporate a tip mass onto the cantilever beam to enhance the sensitivity of the FBG accelerometer. In comparison to the SDOF model, the FBG-MMTP accurately predicts the wavelength shift response and demonstrates good experimental agreement. In addition, the characteristics of wavelength shift response are consistent with the three regions of a fundamental character accelerometer, namely transmission, amplification, and isolation. Since the correlation between the sensitivity determined by FBG-MMTP and the results of the experiment are reasonably agreed, it is justifiable to consider one-half of the fundamental frequency when determining the sensitivity using a polynomial function. In sensitivity identification, it has been demonstrated that neural networks effectively identify the sensitivity of cantilever FBG accelerometers without and with tip mass, addressing frequency-dependent characteristics for dynamic measurements. The approach proves superior to traditional linear methods, offering increased accuracy by accounting for the FBG's sensitivity variations with changing frequencies. The outcomes of this study can be effectively applied in structural health monitoring systems, enabling more accurate detection of low-frequency vibrations in bridges and pipelines.