From Scheduled to Predictive Maintenance: The Energy Sector's Digital Transformation

The energy sector is undergoing extensive change where maintenance work is evolving from traditional scheduled maintenance to more data-driven and proactive methods such as condition-based and predictive maintenance. This development is crucial for meeting both the economic and environmental demands placed on the industry in line with electrification.

The traditional method for maintenance is based on fixed time intervals for inspection and measures, regardless of the equipment’s actual condition. This provides a certain predictability, but is fundamentally a reactive strategy.

The Method:
• Regular inspections according to predetermined intervals
• Standardised protocols and routines
• Based on general service life data

Challenges:
• Inspections often occur without genuine need
• Risk that faults occur between inspection intervals
• Over-maintenance of certain components, under-maintenance of others
• Unnecessary costs and inefficient resource utilisation

Whilst the method facilitates planning, it limits opportunities to prevent unplanned downtime and places considerable demands on staff availability.

Condition-based maintenance represents a shift from calendar-driven to needs-driven maintenance. Through sensors and connected equipment, critical parameters can be monitored in real-time, making it possible to act when the need genuinely arises.

The Method:
• Continuous monitoring of operational parameters
• Data-driven decision support based on equipment’s actual condition
• Use of sensors and IoT solutions
• Real-time data from the field

Advantages:
• Maintenance carried out at the right time
• Early detection of deviations increases operational reliability
• More efficient engineering resource usage and risk reductions
• Reduced environmental impact
• Extended equipment service life

Challenges:
• Requires structure for handling and interpreting data
• Generates large amounts of data that need analysing to create value
• Can lead to many parallel deviations without clear prioritisation
• Provides limited lead time compared to predictive methods
• Cybersecurity is a central element in a connected environment

Condition-based maintenance is an important step towards increased efficiency, and optimising maintenance strategy in the long term.

Predictive maintenance takes the next step by not only monitoring condition but also predicting future faults before they occur. By combining real-time data with historical data, pattern recognition, and AI-based analysis, potential problems can be foreseen before they lead to operational disruptions.

The Method:
• Advanced analysis and machine learning for prediction
• Combination of real-time data, historical patterns, and external factors
• Continuous optimisation of AI models
• Proactive identification of risks

Advantages:
• Problems addressed before they affect operations
• Maximised availability and reliability
• Optimised planning and resource utilisation
• Extended service life for installations
• Reduced environmental impact through better forward planning
• Creates foundation for strategic decision-making
• Strengthens resilience in an increasingly complex electricity grid

Challenges:
• Requires robust data and IT infrastructure
• Requires specialist expertise in data analysis and AI
• System integration can be complex

Predictive maintenance represents a long-term step towards sustainable, resilient, and business-critical maintenance processes – where analysis not only tells what has happened, but what will happen.

For many players in the energy industry, the journey has already begun. Gradual development from scheduled to condition-based and further to predictive maintenance makes it possible to meet today’s and tomorrow’s demands for reliability, efficiency, and sustainability.

It is no longer a question of whether one should digitalise maintenance, but how far one wishes to reach. Predictive maintenance makes it possible to move from putting out fires – to avoiding them entirely.