Document Type
Thesis
Degree
Master of Science (MS)
Major/Program
Electrical Engineering
First Advisor's Name
Ou Bai
First Advisor's Committee Title
Committee Chair
Second Advisor's Name
Shekhar Bhansali
Second Advisor's Committee Title
Committee Co-Chair
Third Advisor's Name
Aparna Aravellia
Third Advisor's Committee Title
Committee Member
Fourth Advisor's Name
Leonel Lagos
Fourth Advisor's Committee Title
Committee Member
Keywords
anomaly detection, piping systems, autoencoder, lstm, machine learning, fiber optics, acoustic sensors, structural health monitoring, nuclear
Date of Defense
7-8-2020
Abstract
Structural health monitoring (SHM) comprises multiple methodologies for the detection and characterization of stress, damage, and aberrations in engineering structures and equipment. Although, standard commercial engineering operations may freely adopt new technology into everyday operations, the nuclear industry is slowed down by tight governmental regulations and extremely harsh environments. This work aims to investigate and evaluate different sensor systems for real-time structural health monitoring of piping systems and develop a novel machine learning model to detect anomalies from the sensor data. The novelty of the current work lies in the development of an LSTM-autoencoder neural network to automate anomaly detection on pipelines based on a fiber optic acoustic transducer sensor system. Results show that pipeline events and faults can be detected by the MLM developed, with a high degree of accuracy and low rate of false positives even in a noisy environment near pumps and machinery.
Identifier
FIDC009175
Recommended Citation
Thompson, Michael, "Structural Health Monitoring of Pipelines in Radioactive Environments Through Acoustic Sensing and Machine Learning" (2020). FIU Electronic Theses and Dissertations. 4458.
https://digitalcommons.fiu.edu/etd/4458
Included in
Acoustics, Dynamics, and Controls Commons, Electromagnetics and Photonics Commons, Industrial Technology Commons, Signal Processing Commons, Systems Engineering Commons
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