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Predictive Maintenance in Power - Thematic Intelligence

Publisher GlobalData
Published Feb 09, 2023
Length 59 Pages
SKU # GBDT17798785

Description

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.
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.

Table of Contents

59 Pages
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

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