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Full Description
Maintenance combines various methods, tools, and techniques in a bid to reduce maintenance costs while increasing the reliability, availability, and security of equipment. Condition-based maintenance (CBM) is one such method, and prognostics forms a key element of a CBM program based on mathematical models for predicting remaining useful life (RUL). Prognostics and Remaining Useful Life (RUL) Estimation: Predicting with Confidence compares the techniques and models used to estimate the RUL of different assets, including a review of the relevant literature on prognostic techniques and their use in the industrial field. This book describes different approaches and prognosis methods for different assets backed up by appropriate case studies.
FEATURES
Presents a compendium of RUL estimation methods and technologies used in predictive maintenance
Describes different approaches and prognosis methods for different assets
Includes a comprehensive compilation of methods from model-based and data-driven to hybrid
Discusses the benchmarking of RUL estimation methods according to accuracy and uncertainty, depending on the target application, the type of asset, and the forecast performance expected
Contains a toolset of methods and a way of deployment aimed at a versatile audience
This book is aimed at professionals, senior undergraduates, and graduate students in all interdisciplinary engineering streams that focus on prognosis and maintenance.
Contents
1. Information in Maintenance 2. Predictive Maintenance Programs and Servitization Maintenance as a Service (MaaS) Creating Value through Prognosis Capabilities 3. RUL Estimation Powered by Data-Driven Techniques 4. Context Awareness and Situation Awareness in Prognostics 5. Black Swans and Physics of Failure 6. Hybrid Prognostics Combining Physics-Based and Data-Driven Approaches 7. Prognosis in Prescriptive Analytics 8. Uncertainty Management and the Confidence of RUL Predictions 9. RUL Estimation of Dynamic and Static Assets 10. Principles of Digital Twin 11. Application of Prognosis in Industry, Energy, and Transportation
