Muhammad Hussain, Tieling Zhang

ISBN: 9789819680184
Published: September 2025
Format: Hardcover
Language: English
Publisher: Springer Singapore

Description:

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. Drawing on global experience in oil & gas, energy and petrochemical sectors, Dr. Muhammad Hussain and Dr. Tieling Zhang 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; address data quality and bias; design predictive maintenance programs; visualize results for decision-makers; and assess future innovations—including blockchain—for traceability and assurance.

About the Authors

Dr. Muhammad Hussain is a consultant specializing in Asset Management, Reliability, Predictive Analytics, and Pipeline Integrity. He applies machine learning, predictive analytics, and risk-based approaches to optimize asset performance, mitigate risk, and enhance operational efficiency, with multiple publications and industry projects worldwide.

Dr. Tieling Zhang collaborates on data-driven integrity and reliability topics across the energy sector, focusing on modeling, inspection data fusion, and decision support for integrity programs.

Table of Contents

• Introduction (Hussain, Zhang) — pp. 1–15
• Fundamentals of Big Data Analytics in the Energy Sector — pp. 17–27
• Data Collection Methods in Pipeline Integrity Management — pp. 29–45
• Data Integration and Preprocessing Techniques — pp. 47–58
• Literature Review — pp. 59–103
• Using Big Data Analytics in PIMS — pp. 105–144
• Data Quality Issues in Model Testing — pp. 145–182
• Energy Pipeline Defect Growth Prediction Using Degradation Modeling — pp. 183–227
• Predictive Maintenance and Pipeline Integrity — pp. 229–249
• Machine Learning Applications in Pipeline Integrity Management — pp. 251–265
• Risk Assessment and Big Data Analytics — pp. 267–276
• Data Visualization and Reporting for Pipeline Integrity — pp. 277–287
• The Role of the Internet of Things (IoT) in Pipeline Integrity Management — pp. 289–300
• Leveraging Blockchain for Pipeline Integrity Management — pp. 301–312
• Future Trends and Innovations in Pipeline Integrity Management — pp. 313–330

Why buy this book?

• Bridges classic integrity practices with Big Data, ML, IoT and blockchain to enable predictive, risk-based decisions.
• Actionable methods: data collection, integration, data quality, defect-growth modeling, maintenance and visualization.
• Direct applicability for energy and oil & gas operators seeking to reduce failures, costs and downtime while improving safety.
• Written by experienced specialists combining research rigor with real-world projects and case-based insight.

Keywords:

big data analytics, pipeline integrity management, energy pipelines, asset management, predictive maintenance, machine learning, Internet of Things (IoT), blockchain, corrosion management, defect growth prediction, risk assessment, data visualization, data quality, pipeline inspection, reliability engineering

Target Audience:

pipeline engineers, asset integrity managers, reliability engineers, data scientists in energy sector, oil & gas consultants, maintenance engineers, researchers in energy and infrastructure, IoT specialists, risk assessment professionals, graduate students in pipeline systems

Genre:

Professional & Technical, Energy Engineering, Oil & Gas Infrastructure, Data Science & Analytics, Asset Management, Industrial Technology

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