Master of Science (MS)
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anomaly detection, piping systems, autoencoder, lstm, machine learning, fiber optics, acoustic sensors, structural health monitoring, nuclear
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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.
Thompson, Michael, "Structural Health Monitoring of Pipelines in Radioactive Environments Through Acoustic Sensing and Machine Learning" (2020). FIU Electronic Theses and Dissertations. 4458.
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