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Big data analytics in energy pipeline integrity management

Record Type:	Electronic resources : Monograph/item
Title/Author:	Big data analytics in energy pipeline integrity management / by Muhammad Hussain, Tieling Zhang.
Author:	Hussain, Muhammad.
other author:	Zhang, Tieling.
Published:	Singapore :Springer Nature Singapore : : 2025.,
Description:	xxv, 330 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Pipelines - Maintenance and repair -
Online resource:	https://doi.org/10.1007/978-981-96-8019-1
ISBN:	9789819680191

Big data analytics in energy pipeline integrity management
Hussain, Muhammad.

Big data analytics in energy pipeline integrity management [electronic resource] /by Muhammad Hussain, Tieling Zhang. - Singapore :Springer Nature Singapore :2025. - xxv, 330 p. :ill., digital ;24 cm. - Lecture notes in energy,v. 1042195-1292 ;. - Lecture notes in energy ;v. 104..

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.

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).

ISBN: 9789819680191

Standard No.: 10.1007/978-981-96-8019-1doiSubjects--Topical Terms:

3791478
Pipelines
--Maintenance and repair

LC Class. No.: TA660.P55

Dewey Class. No.: 621.86720288

Big data analytics in energy pipeline integrity management
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505 0 $a 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.
520 $a 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).
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