Data Driven Energy Centered Maintenance
Over recent years, many new technologies have been introduced to drive the digital transformation in the building maintenance industry. The current trend in digital evolution involves data-driven decision making which opens new opportunities for an energy centered maintenance model. Artificial Intelligence and Machine Learning are helping the maintenance team to get to the next level of maintenance intelligence to provide real-time early warning of abnormal equipment performance.

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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Data Driven Energy Centered Maintenance
Over recent years, many new technologies have been introduced to drive the digital transformation in the building maintenance industry. The current trend in digital evolution involves data-driven decision making which opens new opportunities for an energy centered maintenance model. Artificial Intelligence and Machine Learning are helping the maintenance team to get to the next level of maintenance intelligence to provide real-time early warning of abnormal equipment performance.

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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Data Driven Energy Centered Maintenance

Data Driven Energy Centered Maintenance

Data Driven Energy Centered Maintenance

Data Driven Energy Centered Maintenance

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Overview

Over recent years, many new technologies have been introduced to drive the digital transformation in the building maintenance industry. The current trend in digital evolution involves data-driven decision making which opens new opportunities for an energy centered maintenance model. Artificial Intelligence and Machine Learning are helping the maintenance team to get to the next level of maintenance intelligence to provide real-time early warning of abnormal equipment performance.

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.


Product Details

ISBN-13: 9788770043014
Publisher: River Publishers
Publication date: 10/21/2024
Series: Energy Management
Pages: 266
Product dimensions: 6.12(w) x 9.19(h) x (d)

About the Author

Fadi S. Alshakhshir works in Corporate Facilities Management, in Emaar Properties PJSC, Dubai, United Arab Emirates. He graduated from Jordanian University of Science and Technology with a Mechanical Engineering Degree, and from Heriot Watt University with a Master of Science in Energy, he also has multiple professional certifications such as LEED Green Associate, Certified Energy Manager, Reliability-Centered Maintenance Facilitator, Six Sigma Black-belt.. With over 15 years of practical experience in design, construction, operation, and management of different types of facilities such as hospitals, hotels, malls, residential properties and iconic buildings. He is always thinking of more efficient ways of managing facilities and buildings.

Marvin T. Howell is a former senior energy and solar consultant with SkyPower Energy Corporation in Arlington, Texas. As a contracted senior environmental associate with Analytical Services Inc. for eight years, Mr. Howell implemented environmental management systems at eight U.S. DEA facilities, including labs, division offices, an intelligence center and an air operations center. Previously Mr. Howell was manager of distribution planning and reliability for Florida Power and Light, and also served as a Lt. Colonel in Air Force civil engineering, where he was involved in energy reduction efforts, reliability centered maintenance, project management, construction and maintenance management, and efficiency and productivity improvements. He holds a BS degree in mechanical engineering from Mississippi State University and a master’s in industrial engineering from the University of Pittsburgh. In 2014 he authored the book, Effective Implementation of ISO 50001 Energy Management System, published by ASQ Quality Press. In 2015, he authored the books, Energy Centered Management, A Guide to Reducing Energy Consumption and Cost, published by the Fairmont Press, Inc. and The Results Facilitator, Expert, Manager, Mentor published by CRC Press.

Table of Contents

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

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