Risk-Based Maintenance for Electricity Network Organizations

• Κύριος συγγραφέας: Mehairjan, Ravish Preshant Yashraj (Συγγραφέας)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2017.
• Θέματα: Energy.; Energy policy.; Energy and state.; Energy systems.; Energy security.; Quality control.; Reliability.; Industrial safety.; Energy Policy, Economics and Management.; Quality Control, Reliability, Safety and Risk.; Energy Systems.; Energy Security.

 This book focuses on the introduction of new and modern maintenance management frameworks of assets in the electricity & gas network sector and more specifically, on electricity networks for distribution. The author describes methodologies for developing and implementing maintenance management maturity models, using case studies to show how these have been applied. These maturity models are discussed as part of an overarching, multi-disciplinary organizational maintenance management professionalization framework. This book adds a new dimension to the well-known Reliability Centered Maintenance (RCM) method, by incorporating failure modes via multiple scenarios into business values, by means of statistical risk calculation methods. The author demonstrates a method called Utility Risk Linked RCM, which uses a statistical tool to develop failure models which can be used to predict future failure behavior of assets in relation to corporate business values. This new method is a practical, structured and comprehensive framework for assessing risk based maintenance policies. The book also proposes a condition monitoring framework that can be used as a guide to assist asset managers in identifying the relationship between failure modes, ageing processes to select amongst condition monitoring regimes. 

 Introduction -- Asset, Risk & Maintenance Management -- Organisation-Wide Maintenance Improvement Framework -- Risk Linked Reliability Centered Maintenance Management Model -- Statistical-Based Computational Tools for Maintenance Management -- Condition Monitoring Framework for Maintenance Management -- Conclusions & Recommendations -- Appendices.