Predictive Maintenance in Power – Thematic Intelligence

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Predictive Maintenance in Power Market Thematic Overview

The application of predictive maintenance is useful for fixing irregularities and deficiencies before any equipment fails thus avoiding unnecessary and unplanned reactive maintenance. This way the amount of upkeep and repairs needed can be kept low. Predictive maintenance uses data analysis tools and techniques to detect anomalies and defects. Furthermore, the adoption of predictive maintenance technology will deliver failure prediction, fault diagnosis, failure-type classification, and the recommendation of relevant maintenance actions.

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 predictive maintenance in power thematic intelligence report assesses how predictive maintenance, in conjunction with other emerging technologies, can be used across the power value chain. It provides an overview of the current landscape and key players and highlights opportunities for the use of predictive maintenance in the future. The report also provides an industry-specific analysis based on GlobalData databases and surveys.

Report Pages 56
Regions Covered Global
Value Chain Device Layer, Connectivity Layer, Data Layer, Services Layer, And App Layer
Leading Power Utility Companies American Electric Power, Duke Energy Corporation, and EDF Energy Ltd

Predictive Maintenance in Power – Key Trends

The main trends shaping the predictive maintenance theme over the next 12 to 24 months are classified into two categories: technology trends and macroeconomic trends.

  • Technology trends: Some of the key technology trends impacting the predictive maintenance theme are digitization, application of digital twin, the Internet of Things (IoT), combination of multiple technologies, predictive maintenance data analytics, and augmented reality/virtual reality (AR/VR) technologies.
  • Macroeconomic trends: The key macroeconomic trends explained in the report are enhancing sustainability and ESG.

Predictive Maintenance in Power – Industry Analysis

Predictive maintenance is a proactive maintenance strategy that utilizes condition monitoring tools to detect various signs of deterioration, anomalies, and equipment performance issues. Depending upon the measurements, the companies can run predictive algorithms to estimate when equipment might fail so that maintenance work can be performed just ahead of time, thereby optimizing the usage of maintenance resources. The successful implementation of predictive maintenance will lower operational costs, minimize downtime of assets, and improve overall asset performance.

The predictive maintenance in power industry analysis also covers:

  • Predictive maintenance to deliver efficient power generation
  • Renewables will benefit from predictive maintenance
  • Better management of electrical grids
  • Predictive maintenance service providers
  • Mergers and acquisitions
  • Patent trends
  • Company filing trends
  • Use cases
  • Timeline

For more industry insights into predictive maintenance in power, download a free report sample

Predictive Maintenance in Power - Value Chain Analysis

The predictive maintenance value chain can be categorized into the device layer, connectivity layer, data layer, services layer, and app layer.

Device layer: The device layer majorly involves hardware manufacturers that collect and analyze data on machinery vibrations, heat signatures, metrology, and several other metrics. The companies that cater to this segment include makers of semiconductors and electronic devices and manufacturers of instrumentation for the measurement and analysis of sensor data. The power industry is increasingly switching to the remote monitoring of equipment and prompt scheduling of maintenance activities to ensure operational reliability in remote assets. Hence, vendors offer devices that allow connectivity to a wired or wireless network to transmit data in real time. This aids the quick analysis of the data to take actionable insights.

Predictive Maintenance Value Chain Analysis

Predictive Maintenance Value Chain Analysis

For more insights into the predictive maintenance value chain, download a free report sample

Leading Power Utility Companies

Some of the power utility companies adopting the application of predictive maintenance as a theme are:

  • American Electric Power
  • Duke Energy Corporation
  • EDF Energy Ltd

To know more about the leading power utility companies, download a free report sample

Power Sector Scorecard

At GlobalData, we use a scorecard approach to predict tomorrow’s leading companies within each sector. Our power sector scorecard has three screens: a thematic screen, a valuation screen, and a risk screen.

  • The thematic screen ranks companies within a sector based on their competitive position in the ten themes that matter most to their industry, generating a leading indicator of future performance.
  • The valuation screen ranks companies within a sector based on selected consensus valuation metrics.
  • The risk screen ranks companies within a sector based on overall investment risk.

Power Sector Scorecard – Thematic Screen

Power Sector Scorecard – Thematic Screen

To know more about the sector scorecards, download a free report sample


  • 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.

Reasons to Buy

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.

American Electric Power
Duke Energy
Orsted AS
Southern Company
General Electric
Microsoft Corporation
Schneider Electric
Siemens AG
Hitachi Energy
CentrePoint Energy
Tata Power
China Southern Power Grid (CSG)
Jiangsu Electric Power Company
Dubai Electricity and Water Authority (DEWA)
Exelon Corporation

Table of Contents

  • 1. Executive Summary
  • 2. Players
  • 3. Technology Briefing

    • 3.1. Evolution of maintenance: from reactive to proactive
    • 3.2. Predictive maintenance technologies in the power industry
    • 3.3. Setting up a predictive maintenance system

  • 4. Trends

    • 4.1. Technology trends
    • 4.2. Macroeconomic trends

  • 5. Industry Analysis

    • 5.1. A drive toward a proactive maintenance strategy
    • 5.2. Predictive maintenance to deliver efficient power generation
    • 5.3. Renewables will benefit from predictive maintenance
    • 5.4. Better management of electrical grids
    • 5.5. Predictive maintenance service providers
    • 5.6. Mergers and acquisitions
    • 5.7. Patent trends
    • 5.8. Company filing trends
    • 5.9. Use cases
    • 5.10. Timeline

  • 6. Value Chain

    • 6.1. Device layer
    • 6.2. Connectivity layer
    • 6.3. Data layer
    • 6.4. App layer
    • 6.5. Service layer

  • 7. Companies

    • 7.1. Power companies

  • 8. Sector Scorecard

    • 8.1. Power sector scorecard

  • 9. Glossary
  • 10. Further Reading
  • 11. Our Thematic Research Methodology
  • 12. About GlobalData
  • 13. Contact Us

Frequently asked questions

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