Big Data Analytics in Energy Pipeline Integrity Management
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Beschreibung
This book offers a comprehensive exploration of the integration of Big Data analytics into the management of energy pipeline integrity. Its primary aim is to enhance pipeline safety, reduce operational costs, and ensure long-term sustainability by leveraging data-driven technologies in the monitoring and maintenance of pipelines. Aimed at professionals and researchers in the energy, oil, and gas sectors, as well as those involved in infrastructure management and data science, the book presents how emerging technologies, such as Big Data, Machine Learning (ML), Internet of Things (IoT), and Artificial Intelligence (AI), can revolutionize pipeline integrity management systems (PIMS).
This book offers a comprehensive exploration of the integration of Big Data analytics into the management of energy pipeline integrity. Its primary aim is to enhance pipeline safety, reduce operational costs, and ensure long-term sustainability by leveraging data-driven technologies in the monitoring and maintenance of pipelines. Aimed at professionals and researchers in the energy, oil, and gas sectors, as well as those involved in infrastructure management and data science, the book presents how emerging technologies, such as Big Data, Machine Learning (ML), Internet of Things (IoT), and Artificial Intelligence (AI), can revolutionize pipeline integrity management systems (PIMS).
Bridges the gap between traditional pipeline integrity management (PIM) techniques and cutting-edge Big Data analytics Provides practical applications for detecting pipeline anomalies, forecasting failures, and predicting degradation Includes detailed explanations of ML algorithms, offering insights on applying them to real-world pipeline management
Autor*in
Muhammad Hussain
Themen in »Big Data Analytics in Energy Pipeline Integrity Management«
Data Modeling Fault Detection Model Fittings Analysis Machine Learning (ML) Artificial Intelligence (AI) Asset Management Degradation Models Predictive Maintenance Risk Assessment Internet of Things (IoT)