Buch, Englisch, 266 Seiten, Format (B × H): 242 mm x 160 mm, Gewicht: 650 g
Reihe: Energy Management
Buch, Englisch, 266 Seiten, Format (B × H): 242 mm x 160 mm, Gewicht: 650 g
Reihe: Energy Management
ISBN: 978-87-7022-357-7
Verlag: River Publishers
This edition follows the same methodology as the First. It provides detailed descriptions of the latest technologies associated with Artificial Intelligence and Machine Learning which enable data-driven decision-making processes about the equipment’s operation and maintenance.
Technical topics discussed in the book include:
- Different Maintenance Types and The Need for Energy Centered Maintenance
- The Centered Maintenance Model
- Energy Centered Maintenance Process
- Measures of Equipment and Maintenance Efficiency and Effectiveness
- Data-Driven Energy Centered Maintenance Model:
- Digitally Enabled Energy Centered Maintenance Tasks
- Artificial Intelligence and Machine Learning in Energy Centered Maintenance
- Model Capabilities and Analytics Rules
- Building Management System Schematics
The book contains a detailed description of the digital transformation process of most of the maintenance inspection tasks as they move away from being manually triggered. The book is aimed at building operators as well as those building automation companies who are working continuously to digitalize building operation and maintenance procedures. The benefits are reductions in the equipment failure rate, improvements in equipment reliability, increases in equipment efficiency and extended equipment lifespan.
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Fachgebiete
Weitere Infos & Material
Preface 1.Energy Reduction 2. Different Maintenance Types and The Need for ECM 3.Energy Centered Maintenance Origin and Model 4–9. The ECM Process 10.Relationship Between Maintenance and Low Delta T Syndrome in Chilled Water Systems 11.Energy Centered Maintenance at Data Centers 12.Measures of Equipment and Maintenance Efficiency and Effectiveness 13.Energy Savings Verification 14. Building Energy Centered Behavior Leading to The Energy Awareness & Reduction Culture 15.Data Driven Energy Centered Maintenance Model 16.Conclusion