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Ai-Driven Energy Centered Maintenance
Ai-Driven Energy Centered Maintenance
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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…

Ai-Driven Energy Centered Maintenance (el. knyga) (skaityta knyga) | knygos.lt

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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.

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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.

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