Fadi Alshakhshir – författare

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.

This third edition provides a comprehensive update and expansion on the principles and practices of Energy-Centered Maintenance (ECM). It combines advanced concepts with actionable strategies to enhance equipment energy efficiency, reliability, and availability. By integrating cutting-edge developments in artificial intelligence, machine learning, and predictive maintenance, the book offers a modernized approach to facility management that aligns with digital transformation trends.Designed as a self-study guide and reference for energy engineers, facility managers, and maintenance professionals, this edition provides invaluable knowledge on data-driven and AI-driven maintenance strategies, energy performance optimization, and the integration of smart technologies into maintenance planning. Through practical insights, case studies, and real-world applications, it emphasizes sustainability, operational excellence, and cost-efficiency.Tailored for engineers, facility managers, students, and decision-makers, this edition equips readers with the tools and knowledge needed to implement ECM effectively in modern facilities. It bridges theoretical foundations with real-world applications, demonstrating ECM’s critical role in reducing energy consumption, enhancing equipment performance, and supporting sustainable, AI-driven facility management practices.

This third edition provides a comprehensive update and expansion on the principles and practices of Energy-Centered Maintenance (ECM). It combines advanced concepts with actionable strategies to enhance equipment energy efficiency, reliability, and availability. By integrating cutting-edge developments in artificial intelligence, machine learning, and predictive maintenance, the book offers a modernized approach to facility management that aligns with digital transformation trends.Designed as a self-study guide and reference for energy engineers, facility managers, and maintenance professionals, this edition provides invaluable knowledge on data-driven and AI-driven maintenance strategies, energy performance optimization, and the integration of smart technologies into maintenance planning. Through practical insights, case studies, and real-world applications, it emphasizes sustainability, operational excellence, and cost-efficiency.Tailored for engineers, facility managers, students, and decision-makers, this edition equips readers with the tools and knowledge needed to implement ECM effectively in modern facilities. It bridges theoretical foundations with real-world applications, demonstrating ECM’s critical role in reducing energy consumption, enhancing equipment performance, and supporting sustainable, AI-driven facility management practices.

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 MaintenanceThe Centered Maintenance ModelEnergy Centered Maintenance Process Measures of Equipment and Maintenance Efficiency and EffectivenessData-Driven Energy Centered Maintenance Model:Digitally Enabled Energy Centered Maintenance TasksArtificial Intelligence and Machine Learning in Energy Centered MaintenanceModel Capabilities and Analytics RulesBuilding 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.

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.

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 MaintenanceThe Centered Maintenance ModelEnergy Centered Maintenance Process Measures of Equipment and Maintenance Efficiency and EffectivenessData-Driven Energy Centered Maintenance Model:Digitally Enabled Energy Centered Maintenance TasksArtificial Intelligence and Machine Learning in Energy Centered MaintenanceModel Capabilities and Analytics RulesBuilding 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.