Springer Singapore
Big Data Analytics in Energy Pipeline Integrity Management
Big Data Analytics in Energy Pipeline Integrity Management
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Authors: Muhammad Hussain, Tieling Zhang
ISBN: 9789819680184
Published: September 2025
Format: Hardcover
Language: English
Publisher: Springer Singapore
Big Data Analytics in Energy Pipeline Integrity Management presents a comprehensive, practice-oriented framework to move from reactive maintenance to predictive, data-driven decision-making across the energy pipeline lifecycle. The authors integrate asset integrity management, reliability engineering and risk-based inspection with machine learning, IoT and advanced analytics. Readers learn how to collect, integrate and clean pipeline data, model degradation and defect growth, and design predictive maintenance programs.
Contents: Fundamentals of Big Data Analytics in the Energy Sector; Data Collection and Integration; Energy Pipeline Defect Growth Prediction; Predictive Maintenance; Machine Learning Applications; Risk Assessment; The Role of IoT; Leveraging Blockchain for Pipeline Integrity; Future Trends and Innovations.
Where can I buy Big Data Analytics in Energy Pipeline Integrity Management?
You can order this title directly from CLNZ Books at clnzbooks.com, with worldwide shipping via trusted international courier and payment by card or PayPal.
Q&A
Who is this book for?
Pipeline engineers, asset integrity managers, reliability engineers and oil and gas consultants.
What technologies does it integrate?
Machine learning, IoT and blockchain alongside classic asset integrity and risk-based inspection practices.
Does it include practical methods?
Yes, covering data collection, integration, defect-growth modeling, predictive maintenance and visualization.
What's the book's core goal?
Moving pipeline operators from reactive maintenance to predictive, data-driven decision-making.
Who are the authors?
Muhammad Hussain and Tieling Zhang, both specialists in asset integrity and reliability engineering.
How to Order
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