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. Cover Data Driven Energy Centered Maintenance Data Driven Energy Centered Maintenance Table of Contents Dedication Preface Glossary List of Tables List of Figures 1 Energy Reduction 1.1 Energy Cost 1.2 Implementing Low Hanging Fruit 1.3 Identifying Energy Waste Brainstorming Sessions: 1.4 Energy Conservation 1.5 Energy Efficiency Projects 2 Different Maintenance Types andthe Need for Energy CenteredMaintenance 2.1 History of Maintenance 2.2 The Maintenance Types 3 Energy Centered MaintenanceOrigin and Model 3.1 Origin of ECM 3.2 The Model − Its Aim and Design 3.3 Objectives of ECM 4 ECM Process – Equipment Identification 4.1 Step 1: Equipment Identification 4.2 List of Energy-Related Systems 4.3 Energy Classification Code 5 ECM Process – Data Collection 5.1 Step 2: Data Collection and EquipmentOperational Baseline 5.2 Types of Data 5.3 Sources of Data 6 ECM Process – ECM Inspections 6.1 Step 3: Identify ECM Inspections, Frequency,Craft, Tools, and Job Duration 6.2 Maintenance Records 6.3 Energy Centered Maintenance Inspections 6.4 Energy Centered Maintenance Inspection Frequency 6.5 Energy Centered Maintenance Craft, Tool, and Duration 6.6 Calibration Program 6.7 Inspection Duration 6.8 Energy Centered Maintenance Inspection Plans 7 ECM Process – Measuring Equipment Current Performance 7.1 Step 4: Measuring Equipment’s Current Performance and Comparing to Baseline 7.2 Measuring Equipment’s Current Performance 8 ECM Process – Identifying Corrective/Preventive Action and Cost Effectiveness 8.1 Step 5: Identifying Corrective/Preventive Action andCost Effectiveness 8.2 Identifying Corrective/Preventive Action 8.3 Identifying Cost Effectiveness 8.4 Restoring Equipment Efficiency 9 ECM Process – Updating Preventative Maintenance Plans 9.1 Step 6: Updating PM Plans on CMMS 9.2 What is CMMS? 9.3 Updating PM Plans on CMMS 9.4 Planning and Scheduling Next Inspection 9.5 Sample Problem, Cause, Effect, and Corrective/Preventive Actions 10 Energy Centered Maintenance toavoid Low Delta T Syndrome in Chilled Water Systems 10.1 Low Delta T Syndrome Described 10.2 Maintenance Relationship 10.3 Causes Can Be Avoided During Design Stage 10.4 Causes Can Be Avoided DuringOperation and Maintenance 11 Energy Centered Maintenance in Data Centers 11.1 ECM Terminology and Characteristics 12 Measures of Equipment and Maintenance Efficiency and Effectiveness 12.1 Lead (Key Performance Indicators) and Lag (KeyResult Indicators) 12.2 Maintenance Group Indicators 12.3 Overall Equipment Efficiency (OEE) 12.4 ECM Inspection 12.5 Indicator Checked 12.6 Target Setting 13 Energy Savings Verification 13.1 Baseline 13.2 Example of Energy Baseline 13.3 Energy Baseline, Energy Targets, and EnergyPerformance Indicators 13.4 Energy Centered Maintenance and EnergyPerformance Indicators 13.5 Savings in Data Center Measures and Verification 13.6 Developing an Electricity Baseline and ReducingEnergy Consumption and Costs − A Case Study 13.7 Energy Baseline 13.8 Energy Benchmarking 13.9 Energy Centered Maintenance Implementation 14 Building Energy Centered Behavior Leading to an Energy Centered Culture 14.1 Kinds of Organizations’ Cultures 14.2 Culture Definition and Building a Specific Culture 15 Data Driven Energy Centered Maintenance Model 15.1 Digital Transformation 15.2 Digitally Enabled Energy CenteredMaintenance Tasks 15.3 Benefits of Data Driven Energy CenteredMaintenance 15.4 Artificial Intelligence and Machine Learning inEnergy Centered Maintenance 15.5 Model Capabilities 15.6 Analytics Rules 15.7 Building Management System Schematics 16 Conclusion 16.1 Designing and Implementing ECM 16.2 Characteristics of a Successful EnergyReduction Program 16.3 Data Driven Energy Centered Maintenance ECM References List of Acronyms Index About the Authors 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