Predictive Maintenance in Power - Thematic Intelligence
Summary
The application of predictive maintenance will have a greater impact on utilities in day-to-day operations. Power utilities deal with the crucial tasks of monitoring and maintaining their assets while ensuring that these assets function at peak efficiency and reliability. Through the use of predictive maintenance technologies, power utilities can detect underperforming assets and enable the operating staff or personnel to understand the factors leading to these abnormal operations, and accordingly schedule maintenance activities. The emergence and swift growth of innovative technologies such as the Internet of Things (IoT), artificial intelligence (AI), augmented and virtual reality (AR and VR), big data, and cloud computing will continue to shape maintenance strategies in the power industry.
Scope
- The report focuses on predictive maintenance in power as a theme.
- It provides an industry analysis on how predictive maintenance drives proactive maintenance strategy and can deliver efficient power generation.
- The report provides an insight on the application of predictive maintenance in renewables and electrical grid.
- It covers patents trends and company filing trends in power.
- The report briefs on growing application of predictive maintenance in the power sector and its use cases in power utilities.
- It contains details of M&A deals driven by predictive maintenance theme, and a timeline highlighting milestones for predictive maintenance.
- The report presents the trends related to predictive maintenance as a theme in technology, and macroeconomic trends.
- The report also includes an overview of competitive positions held by power utility companies adopting predictive maintenance technology.
- The report provides:
- A comprehensive analysis of the emerging market trend of predictive maintenance technology in power sector.
- The report gives an insight of 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 briefing on different predictive maintenance technologies in power industry and detailed analysis of predictive maintenance value chain.
- Company profiles of leading adopters of predictive maintenance technology in power sector.
- An overview of predictive maintenance technology service providers.
- A snapshot of power sector scorecard predicting the position of leading power companies 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
- A drive toward a proactive maintenance strategy
- Predictive maintenance to deliver efficient power generation
- Renewables will benefit from predictive maintenance
- IoT-based predictive maintenance
- AI-driven predictive maintenance
- Better management of electrical grids
- Predictive maintenance service providers
- Mergers and acquisitions
- Patent trends
- Company filing trends
- Use cases
- Duke Energy's predictive analytic system
- EDF’s partnership for predictive maintenance of equipment
- ENGIE’s predictive maintenance solutions
- Enel’s predictive maintenance model
- E.ON’s predictive maintenance to prevent grid failure
- Ørsted’s data-driven approach to enhance productivity and reduce costs
- Timeline
- Value Chain
- Device layer
- Sensors and probes
- Connectivity layer
- Edge and cloud infrastructure
- Networking equipment
- Wireless network
- Data layer
- Data storage
- Data processing and analysis
- Business intelligence
- App layer
- Service layer
- System design and integration
- Inspection and maintenance
- Digital twins
- Companies
- Power companies
- Sector Scorecard
- Power sector 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 service providers
- Table 4: Mergers and acquisitions
- Table 5: Power companies
- Table 6: Glossary
- Table 7: GlobalData reports
- List of Figures
- Figure 1: Who are the leading players in predictive maintenance in the power sector?
- Figure 2: Predictive maintenance through on-site condition monitoring
- Figure 3: Vibration monitoring of rotating equipment
- Figure 4: Infrared thermography of 13.8kV bus bar bushings to generator breaker
- Figure 5: Lubricant oil analysis of rotating equipment
- Figure 6: An example of ultrasonic testing
- Figure 7: The increase in patent activity is driving predictive maintenance
- Figure 8: Predictive maintenance activities are increasing in the power industry
- Figure 9: Companies are increasingly mentioning predictive maintenance in their filings
- Figure 10: An engineer using AVEVA’s predictive analytic software
- Figure 11: Predictive maintenance for hydroelectric power plants
- Figure 12: ENGIE Energia’s digital twin pilot project
- Figure 13: PresAGHO – a predictive maintenance model for hydroelectric power plants
- Figure 14: Application of AI for predictive power grid maintenance
- Figure 15: Ørsted’s predictive maintenance for offshore wind turbines
- Figure 16: The predictive maintenance story
- Figure 17: Interaction of predictive maintenance technologies with the power value chain
- Figure 18: Predictive maintenance value chain
- Figure 19: Predictive maintenance value chain: Device layer
- Figure 20: Predictive maintenance value chain: Connectivity layer
- Figure 21: Predictive maintenance value chain: Data layer
- Figure 22: Predictive maintenance value chain: App layer
- Figure 23: Predictive maintenance value chain: Services layer
- Figure 24: Who does what in the power sector?
- Figure 25: Thematic screen
- Figure 26: Valuation screen
- Figure 27: Risk screen
- Figure 28: Our five-step approach for generating a sector scorecard
Research Assistance
Download our eBook: How to Succeed Using Market Research
Learn how to effectively navigate the market research process to help guide your organization on the journey to success.
Download eBook