Predictive Maintenance in Power - Strategic Intelligence
Summary
Predictive maintenance strategies is enabling power companies to transition from traditional, reactive maintenance models to a more sophisticated, data-driven paradigm. This shift not only optimizes asset performance but also extends the lifespan of critical infrastructure.
Artificial Intelligence (AI) is markedly enhancing predictive maintenance within the power sector. AI-driven systems are increasingly being incorporated into power plants and grids to meticulously monitor equipment, analyze data, and forecast potential malfunctions. This integration facilitates proactive maintenance strategies and aids in averting expensive outages.
Wind and solar photovoltaic (PV) systems are progressively utilizing predictive maintenance to enhance reliability and efficiency.
Scope
- The report focuses on predictive maintenance in power as a theme.
- It provides an industry insight on how predictive maintenance drives proactive maintenance strategy and can deliver efficient power generation.
- The report discusses on how artificial intelligence is driving predictive maintenance in power.
- The report briefs on growing application of predictive maintenance in wind and solar PV and its use cases in power utilities.
- The report delivers an overview on how predictive maintenance can optimize energy storage.
- The report covers mergers & acquisitions (M&As), venture financing deals and patent trends in predictive maintenance.
- The report provides an overview on competitive position held by power utility companies adopting predictive maintenance in business operations.
- A comprehensive analysis on the growing market trend of predictive maintenance in the power industry.
- The report provides an overview on the leading players in predictive maintenance theme and where do they fit in the value chain.
- Technology briefing on reactive approach, preventive approach, condition-based approach and predictive approach maintenance.
- A detailed analysis of predictive maintenance value chain.
- Company profiles of leading power utilities in predictive maintenance.
- An overview on predictive maintenance service and solution providers.
- The report emphasizes the role of artificial intelligence in predictive maintenance and discusses how it will transform solar PV and wind.
- A snapshot of power sector scorecard predicting the position of leading power utilities in predictive maintenance theme.
- Executive Summary
- Players
- Technology Briefing
- Evolution of maintenance: from reactive to proactive
- Reactive approach
- Preventive approach
- Condition-based approach
- Predictive approach
- Predictive maintenance technologies in the power industry
- Vibration monitoring
- Infrared thermography
- Lubricant oil analysis
- Ultrasonic and acoustic emission monitoring
- Setting up a predictive maintenance system
- The importance of predictive maintenance for aging infrastructure
- Trends
- Technology trends
- Macroeconomic trends
- Industry Analysis
- Understanding key principles of predictive maintenance
- How does predictive maintenance work?
- Reasons to adopt predictive maintenance
- Operational efficiency
- Cost savings
- Improved safety
- Sustainable production
- Inventory management
- Improved product quality
- AI-driven predictive maintenance in the power sector
- PdM is gaining ground in solar PV and wind power
- Optimizing energy storage with predictive maintenance
- Predictive maintenance solution providers
- Use cases
- EDF’s predictive maintenance of equipment
- Enel’s predictive diagnostics for batteries
- ENGIE’s predictive maintenance for high voltage (HV) systems
- Evergy’s intelligent substation monitoring
- Iberdrola’s Project WinDTwin
- RWE’s condition monitoring of wind turbines
- Vattenfall’s digital twin for wind farms
- Timeline
- Signals
- M&A trends
- Venture financing trends
- Patent trends
- Value Chain
- Device layer
- Sensors and probes
- Thermal imaging
- Measurement devices and instruments
- Connectivity layer
- Edge and cloud infrastructure
- Networking equipment
- Wireless network
- Data layer
- Data storage
- Data processing and analysis
- Business intelligence
- App layer
- Services layer
- System design and integration
- Inspection and maintenance
- Digital twins
- Companies
- Power companies
- Sector Scorecards
- Power sector scorecard
- Who’s who
- Thematic screen
- Valuation screen
- Risk screen
- Industrial automation scorecard
- Who’s who
- Thematic screen
- Valuation screen
- Risk screen
- Glossary
- Further Reading
- GlobalData reports
- Our Thematic Research Methodology
- About GlobalData
- Contact Us
- List of Tables
- Table 1: Technology trends
- Table 2: Macroeconomic trends
- Table 3: Predictive maintenance solution providers
- Table 4: Key M&A transactions associated with the predictive maintenance theme
- Table 5: Power companies
- Table 6: Glossary
- Table 7: GlobalData reports
- List of Figures
- Figure 1: Who are the leading players in the predictive maintenance theme, and where do they sit in the value chain?
- Figure 2: Predictive maintenance through on-site condition monitoring
- Figure 3: The different maintenance strategies: an overview
- Figure 4: Vibration monitoring of rotating equipment
- Figure 5: Infrared thermography of 13.8kV busbar bushings
- Figure 6: Lubricant oil analysis of rotating equipment
- Figure 7: An example of ultrasonic testing
- Figure 8: Predictive maintenance workflow
- Figure 9: AI-driven predictive maintenance focuses on
- Figure 10: Predictive maintenance of rotating machinery
- Figure 11: Predictive diagnostics of a battery
- Figure 12: Predictive maintenance of HV Systems
- Figure 13: A top view of Evergy’s substation
- Figure 14: An offshore wind power plant site
- Figure 15: An offshore wind turbine being inspected
- Figure 16: A digital display of wind turbine predictive analytics
- Figure 17: The predictive maintenance story
- Figure 18: Predictive maintenance deals in power
- Figure 19: Deal volume by countries for 2021−2024
- Figure 20: Sector-wise deal volume trend in predictive maintenance for 2021−2024
- Figure 21: The increase in patent activity is driving growth in the adoption of predictive maintenance
- Figure 22: Predictive maintenance activities will continue to increase in the power industry
- Figure 23: The predictive maintenance value chain interaction with the power industry value chain
- Figure 24: The predictive maintenance value chain
- Figure 25: Predictive maintenance value chain: Device layer
- Figure 26: The predictive maintenance value chain: Connectivity layer
- Figure 27: The predictive maintenance value chain: Data layer
- Figure 28: The predictive maintenance value chain: App layer
- Figure 29: The predictive maintenance value chain: Services layer
- Figure 30: Who does what in the power space?
- Figure 31: Thematic screen - Power sector scorecard
- Figure 32: Valuation screen - Power sector scorecard
- Figure 33: Risk screen - Power sector scorecard
- Figure 34: Who does what in the power space?
- Figure 35: Thematic screen - Industrial automation scorecard
- Figure 36: Valuation screen - Industrial automation scorecard
- Figure 37: Risk screen - Industrial automation scorecard
- Figure 38: Our five-step approach for generating a sector scorecard
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