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AI and Predictive Maintenance in Intelligent Buildings 2022: Growing Demand for Greater Visibility and Control Around System and Machine Health Drives Sector

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Dublin, Dec. 28, 2022 (GLOBE NEWSWIRE) -- The "AI and Predictive Maintenance in Intelligent Buildings" report has been added to ResearchAndMarkets.com's offering.

This research takes an in-depth look at predictive maintenance and AI in intelligent buildings. Using stakeholder surveys, expert interviews, and detailed market analysis, this project set out to understand how use cases, customer environments, buying behaviors, and ecosystem interactions all impact and influence the development of these technologies.

OVERVIEW OF AI AND PREDICTIVE MAINTENANCE IN BUILDINGS

The wave of applications that will leverage AI and machine learning (ML) to automate basic tasks will disrupt every industry imaginable. Intelligent buildings are no exception, bringing forward key use cases in the areas of maintenance, energy management, financial analytics, and experience orchestration.

Predictive maintenance relies on reactive analytics, as well as multi-regression analysis and convolutional neural networks (CNNs). Regression analysis is a form of supervised ML that predicts the effect that one variable has on another based on how the two variables correlate.

CNNs also depend on supervised ML, but are specifically designed for image recognition. Predictive maintenance can be characterized as a suite of software and platforms tools that leverage data from control and automation systems, distributed sensor networks, and external business intelligence to provide signal from noise estimates of when a system is expected to break down.

The growing demand for greater visibility and control around system and machine health, in conjunction with the increasing availability of emerging technologies, has led to a consistent cycle of innovation and progress around predictive maintenance. This approach effectively identifies the likely issue and estimates the system's life expectancy given the occurrence of that issue.

Most applications of AI for predictive maintenance in buildings are aimed at reducing labor costs, downtime, and the overall duration of the maintenance process. This is largely achieved by predicting a potential system failure and dispatching technicians before that failure occurs. Doing so will likely translate to fewer hours spent diagnosing the issue, and fewer dollars spent replacing machinery that could have otherwise been fixed.


Key Topics Covered:

1. INTRODUCTION: THE EVOLUTION OF AI AND PREDICTIVE MAINTENANCE IN INTELLIGENT BUILDINGS
1.1 What are Intelligent Buildings, and How Have They Evolved Over Time?
1.1.1 The Evolution of Intelligent Buildings
1.1.2 Introduction to Smart Systems
1.1.3 The Evolution of Maintenance Management Best Practices and Technologies
1.1.4 Building Technology Maturity Sets the Stage for Artificial Intelligence
1.2 Predictive Maintenance Needs of Buildings Differ Greatly by Building Type
1.3.1 Technological Innovations Set the Stage for Smart System Applications
1.3.2 Ecosystem Maturity Leading to More Complicated Business Models and Data Sharing Practices
1.3.3 Potential for Further Lockdowns Affects the Distribution of Demand Across Building Types
1.4 Building Types and Case Studies
1.4.1 Medical
1.4.2 Commercial
1.4.3 Retail and Hospitality
1.4.4 Mission Critical
1.4.5 Public Venues
1.4.6 Institutional

2. ARTIFICIAL INTELLIGENCE IS ENABLING A NEW GENERATION OF PREDICTIVE MAINTENANCE APPLICATIONS
2.1 Overview of Artificial Intelligence and Machine Learning
2.1.1 The Intelligent Buildings Data Pipeline
2.1.2 The Artificial Intelligence in Buildings Player Ecosystem
2.2 Use Cases for AI in Intelligent Buildings
2.2.1 AI-Enabled Predictive Maintenance
2.2.2 Energy Analytics and Efficiency Optimization with AI
2.2.3 Optimizing and Automating Building Operations with AI
2.3 Enabling the AI-Powered Intelligent Building
2.3.1 Integrating AI with Building Automation Systems
2.3.2 Security and Data Privacy Must be Considered
2.3.3 AI's Role in the Net-Zero Future

3 REALIZING THE PROMISED VALUE OF PREDICTIVE MAINTENANCE
3.1 Tenant Satisfaction is Tied to How Well Buildings Manage Maintenance Requirements
3.1.1 Maintenance Guarantees as a Factor Affecting Satisfaction
3.1.2 Pain Points and Expectations Surrounding Building Maintenance
3.2 Building Operators Recognize the Need for Smarter Maintenance Management Practice
3.2.1 Operator Frustrations with Buildings Maintenance Practices
3.2.2 Promising Sensor and Data Collection Practices
3.2.3 Value Propositions that Move the Needle
3.3 Specific Fears Must First be Overcome
3.3.1 Operators are Worried About Impact on Employment
3.3.2 Tenant Fears Regarding AI Adoption
3.3.3 Supplier Strategies to Overcome AI Barriers and Catalyze the Market
3.4 Occupant and Operator Willingness to Pay Presents Opportunities for AI and PMIB Ecosystem Participants
3.4.1 Operator Willingness to Pay Increases with Operational Benefits
3.4.2 Tenant Willingness to Pay Allows Buildings to Offset Costs by Raising Rents
3.5 Eliminating Barriers to Adoption
3.5.1 Costs Drive Action
3.5.2 Systems Integration and Data Management are Costly Hindrances

4. PREDICTIVE MAINTENANCE IN INTELLIGENT BUILDINGS
4.1 Predictive Maintenance Solutions Introduction and Overview
4.1.1 Components and Capabilities of Available PMIB Solutions
4.1.2 Supplier Landscape and The Evolution of the PMIB Market
4.1.3 Functions and Features of a Predictive Analytics System
4.2 The Interaction Between Predictive Maintenance and the Components of an Intelligent Buildings.
4.2.1 Heating, Ventilation, and Air Conditioning Systems
4.2.2 Energy Management
4.2.3 Occupant Comfort Systems (Lighting, Shading)
4.2.4 Water Management
4.2.5 Network Infrastructure and Communications
4.2.6 Elevators and Escalators
4.2.7 Electrical Distribution Equipment, Uninterruptible Power Supply (UPS) Systems, And Failover/Disaster Recovery
4.2.8 Structural Integrity
4.3 Technology is Converging to Enable Predictive Maintenance
4.3.1 Automation
4.3.2 Secure Remote Access
4.3.3 Digital Twins
4.3.4 Edge Computing
4.3.5 Cybersecurity

5. DELIVERING A SEAMLESS EXPERIENCE
5.1 Across the PMIB Value Chain, Players Need to Act Now
5.1.1 OEM Strategic Recommendations
5.1.2 Software Provider Strategic Recommendations
5.1.3 Services Provider Strategic Recommendations
5.1.4 Recommendations for Buildings Owners/Property Managers
5.2 Interoperability and Collaboration is the Catalyst
5.3 AI and PMIB Report Conclusion

For more information about this report visit https://www.researchandmarkets.com/r/p0tay7

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