Description
Book SynopsisMaintenance 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
Table of 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
