+353-1-416-8900REST OF WORLD
+44-20-3973-8888REST OF WORLD
1-917-300-0470EAST COAST U.S
1-800-526-8630U.S. (TOLL FREE)

Predictive Maintenance in Power - Thematic Research

  • PDF Icon


  • 42 Pages
  • May 2022
  • Region: Global
  • GlobalData
  • ID: 4829067
1h Free Analyst Time
1h Free Analyst Time

Speak directly to the analyst to clarify any post sales queries you may have.

Predictive maintenance tools assess the condition of operational equipment and allow users to foresee any necessary maintenance requirements, in order to attain optimum performance and avoid potentially costly equipment failures.

Remote monitoring is a crucial element of predictive maintenance, and remote and centralized observation platforms have boosted the decision-making process. There has been a rising interest in decision models for predictive maintenance, triggered by failure predictions. Over the next decade, predictive maintenance tools will become even more widespread across the critical infrastructure in the power industry, as they provide operational and financial fluidity through the use of technology.

Older power plant facilities face the increased risk of unplanned downtime. These may contribute to excess greenhouse gas (GHG) emissions. Using predictive maintenance tools, the performance of older power plant equipment can be enhanced. The COVID-19 pandemic also alerted the power industry to the perils of shortages of skilled maintenance personnel, especially in the case of equipment breakdowns in remote locations. Predictive maintenance can help improve human resource allocation, thereby boosting productivity and enhancing utilities’ financial position and brand value, leading to increased customer satisfaction.

The emergence and swift growth of innovative technologies such as the Internet of Things (IoT), artificial intelligence (AI), augmented and virtual reality (AR/VR), big data, and cloud computing have shaped the maintenance strategies of the power industry. The base measurement technologies for predictive maintenance-such as vibration monitoring and thermal imaging-have also improved, as huge amounts of data and analytical capabilities are available, thanks to the rise in digital transformation projects across the power industry.


  • Overview of the evolution of predictive maintenance as a theme and key technologies employed.
  • Review of application of predictive maintenance strategies in power industry.
  • Detailed analysis of the predictive maintenance value chain, its role within the power value chain, and corresponding participation of major players.
  • Highlighting of the various industry, technology, and macroeconomic trends influencing the predictive maintenance theme.
  • Assessment of the strategies and initiatives adopted by power companies to gain a competitive advantage in this theme.

Reasons to Buy

  • Identify the key industry, technology, and macroeconomic trends impacting the predictive maintenance theme.
  • Deployment of predictive maintenance strategies in power industry.
  • Understand the predictive maintenance value chain and the key players in it.
  • Identify and benchmark key power utility players and power system services companies based on their competitive positioning in the predictive maintenance theme.

Table of Contents

  • Executive Summary
  • Players
  • Tech Briefing
  • Evolution of Maintenance: from Reactive to Proactive
  • Predictive Maintenance Technologies in the Power Industry
  • Setting Up a Predictive Maintenance System
  • Importance of Predictive Maintenance for

Companies Mentioned

A selection of companies mentioned in this report includes:

  • Enel
  • EDF Energy
  • Duke Energy
  • E.ON
  • Southern Company
  • American Electric Power
  • Aker Solutions
  • Emerson
  • GE
  • ABB
  • Honeywell
  • Teledyne FLIR
  • SKF
  • Parker Hannifin
  • Bosch
  • Fluke
  • Vodafone
  • AT&T
  • Verizon
  • Cisco
  • HPE
  • Telenor
  • Amazon
  • Google
  • Microsoft
  • IBM
  • Ericsson
  • Siemens
  • Schneider Electric
  • Rockwell Automation
  • Halliburton
  • Wartsila
  • Kongsberg
  • DNV