Author: Lai, Ho Yin Koey
Title: The applications of deep learning in BOTDA sensing system
Advisors: Yu, Changyuan (EIE)
Degree: M.Sc.
Year: 2020
Subject: Detectors
Machine learning
Hong Kong Polytechnic University -- Dissertations
Department: Department of Electronic and Information Engineering
Pages: vii, 55 pages : color illustrations
Language: English
Abstract: Two deep learning algorithms were applied on the experimental raw data from the Brillouin optical time-domain Analyzer (BOTDA) sensing system respectively. Deep neural network (DNN) was used for estimating the temperatures at each of time step from the raw Brillouin gain spectrum (BGS) profile. The mean absolute error is very small with lesser than 1°C and the network structure of 3 hidden layers outperform at the error of lesser than 0.1°C relatively to the traditional Lorentzian curve fitting (LCF) results. New study was also conducted in the application of deep learning on the pattern recognition for the BOTDA sensing data. The Long short-term memory (LSTM) models were successfully implemented with high accuracy. The dataset used in the study is small and homogeneous. It may not be representative enough. However, several LSTM network structures were studied to explore the direction of adjusting the LSTM model in future studies. Both adding an additional hidden layer with nodes relatively to the input dimension and bidirectional network return better performance than the single LSTM layer model. Further works can add on the strain data or conduct on the on-site data to verify the model performance. It is believed that the deep learning application on BOTDA has good potential in the development of automatic structure health monitoring (SHM).
Rights: All rights reserved
Access: restricted access

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