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Small Leak Detection in Pipelines Using Deep Learning and Statistical Control Chart
Conference paper

Small Leak Detection in Pipelines Using Deep Learning and Statistical Control Chart

Yu-Chen Liang, Yi-Hsiang Cheng, Zhen-Yu Hung and Yuan Yao
Proceedings of 2024 IEEE 13th Data Driven Control and Learning Systems Conference, DDCLS 2024, pp.2133-2138
2024

Abstract

autoencoder control chart industrial safety leak detection Artificial Intelligence Control and Systems Engineering Control and Optimization Modeling and Simulation
Since the late 20th century, the demand for petrochemical materials and products in industry and daily life has increased, leading to the widespread use of pipelines for transporting these products. Recognizing the importance of pipeline safety, many countries have emphasized safety management through the enactment and amendment of laws, requiring business owners to oversee the design, construction, operation, and maintenance of pipelines. Additionally, business owners must ensure the integrity of pipeline systems, undertake autonomous management, perform regular risk assessments, and implement a monitoring and diagnostic system for industrial pipelines. This study seeks to develop an algorithm that outperforms current leak detection systems, enhancing the safety of industrial pipelines.

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