Big data analytics in energy pipeline integrity management / Muhammad Hussain, Tieling Zhang

• Autore: Hussain, Muhammad
• Pubblicazione: Singapore, : Springer, 2025
• Descrizione fisica: 1 testo elettronico (XXV, 330 p. : ill.)
• Soggetto non controllato: Artificial Intelligence; Asset Management; Data Modeling; Degradation Models; Fault Detection; Internet of Things (IoT); Machine Learning (ML); Model Fittings Analysis; Predictive Maintenance; Risk Assessment
• Altri autori: Zhang, Tieling
• Sommario/riassunto: 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). (Dal sito dell'editore)
• Titolo autorizzato: Big Data Analytics in Energy Pipeline Integrity Management
• ISBN: 978-98-19-68018-4
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: VAN00301116
• Lo trovi qui: Univ. Vanvitelli
• Localizzazioni e accesso elettronico: https://doi.org/10.1007/978-981-96-8019-1
• Serie: Lecture notes in energy Cham . -Springer. 2012- ; 104