Teil der Reihe: Energy (R0)
Chapter 1: Introduction.- Chapter 2: Fundamentals of Big Data Analytics in the Energy Sector.- Chapter 3: Data Collection Methods in Pipeline Integrity Management.- Chapter 4: Data Integration and Preprocessing Techniques.- Chapter 5: Literature Review.- Chapter 6: Using Big Data Analytics in PIMS.- Chapter 7: Data Quality Issues in Model Testing.- Chapter 8: Energy Pipeline Defect Growth Prediction Using Degradation Modelling.- Chapter 9: Predictive Maintenance and Pipeline Integrity.- Chapter 10: Machine Learning Applications in Pipeline Integrity Management.- Chapter 11: Risk Assessment and Big Data Analytics.- Chapter 12: Data Visualization and Reporting for Pipeline Integrity.
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
This book serves as a comprehensive resource for professionals involved in the design, inspection, integrity, maintenance, and operation of hydrogen pipelines, addressing the unique challenges hydrogen presents compared to natural gas or other fuels. It covers the latest inspection technologies, risk assessment methodologies, and mitigation strategies to ensure the safety and efficiency of hydrogen pipelines.
