Advanced Analytics for Asset Management
1st Edition
By Ali Soofastaei
Copyright Year ©
ISBN:
Publisher: CRC Press
Published:
Number of Pages:
Access this book on the publisher's site
Description
Book Description
Advanced Analytics for Asset Management explains how data science, artificial intelligence, reliability engineering, and decision intelligence can be applied to improve the performance of physical assets. Covering asset data foundations, predictive maintenance, reliability analytics, machine learning, digital twins, optimisation, lifecycle cost analysis, and analytics governance, the book provides a practical framework for asset-intensive industries seeking to reduce downtime, improve reliability, manage risk, and maximise asset value. It is designed for engineers, asset managers, maintenance professionals, data scientists, digital transformation leaders, researchers, and postgraduate students working at the intersection of analytics and industrial asset performance.
Table of Contents
Chapter 1: Introduction to Advanced Analytics for Asset Management Chapter 2: Principles of Asset Management in Industrial Operations Chapter 3: Reliability Engineering Foundations for Asset Analytics Chapter 4: Asset Data Architecture and Governance Chapter 5: Industrial Data Sources for Asset Management Chapter 6: Preparing Data for Asset Analytics Chapter 7: Descriptive Analytics for Asset Performance Chapter 8: Diagnostic Analytics and Root Cause Identification Chapter 9: Predictive Maintenance and Condition-Based Monitoring Chapter 10: Machine Learning for Asset Failure Prediction Chapter 11: Time-Series Analytics and Remaining Useful Life Estimation Chapter 12: Anomaly Detection and Fault Diagnosis Chapter 13: Maintenance Optimisation and Decision Analytics Chapter 14: Spare Parts, Inventory, and Supply Chain Analytics Chapter 15: Lifecycle Cost, Risk, and Asset Replacement Analytics Chapter 16: Digital Twins for Asset Management Chapter 17: Artificial Intelligence and Generative AI in Asset Management Chapter 18: Asset Performance Management Platforms and System Integration Chapter 19: Building an Asset Analytics Operating Model Chapter 20: Business Value, Benefits Tracking, and Performance Measurement Chapter 21: Maturity Assessment and Roadmap for Analytics-Enabled Asset Management Chapter 22: Future of Advanced Analytics for Asset Management
Editor
Dr. Ali Soofastaei
Biography
​Dr. Ali Soofastaei is a global artificial intelligence (AI) projects leader, an international keynote speaker, and a professional author.
​He completed his Ph.D. and Postdoctoral Research Fellow at The University of Queensland, Australia, in the field of AI applications in mining engineering, where he led a revolution in the use of deep learning and AI methods to increase energy efficiency, reduce operation and maintenance costs, and reduce greenhouse gas emissions in surface mines. As a scientific supervisor, for many years, he has provided practical guidance to undergraduate and postgraduate students in mechanical and mining engineering and information technology.
Dr. Soofastaei has more than fifteen years of academic experience as an Assistant Professor and leader of global research activities. Results from his research and development projects have been published in international journals and keynote presentations; He has presented his practical achievements at conferences in the United States, Europe, Asia, and Australia. He has been involved in industrial research and development projects in several industries, including oil and gas (Royal Dutch Shell); steel (Danieli); and mining (BHP, Rio Tinto, Anglo American, and Vale). His extensive practical experience in the industry has equipped him to work with complex industrial problems in highly technical and multi-disciplinary teams. Dr. Soofastaei is working actively with some prestigious global publishers same as Mc Graw-Hill Education, Intech Open, Springer, and CRC Press as an author and academic editor.
