Definition of predictive maintenance, examples and benefits

In the 19th century, steam locomotives required constant maintenance. Manual lubrication - operators had to walk around the moving parts every few kilometres and check which ones needed oil - it was essential to prevent them from overheating. The machinery used in transport today is more sophisticated, but maintenance is still necessary to avoid breakdowns and, as with the old locomotives, it is more cost-effective to detect faults early. Predictive maintenance helps do this.

In an increasingly digitalised and connected world, the efficient management of industrial and infrastructure assets has become a critical factor in ensuring continuity of service, operational efficiency and resilience against disruptions. The widespread deployment of sensors across equipment and networks, together with the application of artificial intelligence in operations and maintenance, makes it possible to anticipate failures before they occur, optimising resources and reducing costs. In this context, predictive maintenance is not merely a technological tool, but a key strategy for companies seeking to adapt to digital transformation and ensure safer, more sustainable and efficient operations.

What is predictive maintenance?

Predictive maintenance is a technique that uses data analysis tools and methods to detect anomalies in operations and potential defects in equipment and processes, allowing issues to be resolved before a failure occurs. For example, in power generation turbines, the continuous analysis of data such as vibration, temperature or energy consumption can identify wear or misalignments long before a major breakdown takes place. Just as predictive analytics can forecast market movements or fluctuations in energy demand, predictive maintenance uses data analysis to anticipate system failures and is a fundamental part of the Industrial Internet of Things (IIoT).

How predictive maintenance works?

To monitor the condition of equipment and alert technicians to upcoming failures, preventive maintenance has three main components:

• Sensors and connected devices installed on machines send data on machine status and performance in real time thanks to the Internet of Things (IoT), technologies, which enable communication between machines and analytics systems.
• Software solutions and cloud computing allow data mining to be applied to collect and analyse huge amounts of information using big data applications.
• Predictive models are fed with the processed data and use machine learning technologies to establish patterns and comparisons, make predictions of failures and schedule maintenance before they occur.

Technologies that make it possible

Predictive maintenance is made possible through the combination of several technologies. IIoT (Industrial Internet of Things) sensors and connected devices enable real-time monitoring of equipment and processes. The data collected is integrated into data platforms that facilitate processing and analysis, revealing patterns that would otherwise remain undetected. Finally, artificial intelligence and machine learning algorithms learn from equipment history and operating variables to predict future failures and optimise maintenance scheduling.

What are the differences between predictive, preventive and corrective maintenance?

Predictive maintenance is different from preventive and corrective maintenance. However, they can all be used in industry simultaneously. We review their differences below:

• Preventive: consists of inspecting machinery from time to time, whether or not it needs to be inspected, or doing so when some symptom is detected (e.g. a strange noise).
• Corrective: also called reactive or breakdown, it is executed when a fail has already occurred and the damaged equipment needs to be repaired.
• Predictive: a data-driven, proactive maintenance method designed to analyse the condition of equipment on an ongoing basis and foresee potential failures.

In critical infrastructure such as electricity networks, where the unavailability of an asset directly affects system reliability and service quality indicators, predictive maintenance is essential for optimising asset management, reducing the risk of unplanned failures and ensuring operational continuity.

SEE INFOGRAPHIC: When is each type of maintenance implemented and what signs do they detect? [PDF] External link, opens in new window.

Benefits for companies in the energy sector

In the energy sector, efficient maintenance planning across transmission and distribution networks, as well as generation assets, based on predictive maintenance strategies, help optimise equipment availability and significantly reduce unplanned outages. Using real-time data, advanced sensors and predictive models, companies can prioritise interventions according to the actual condition of transformers, lines, substations or turbines, coordinating maintenance activities with system operations. This approach not only improves the reliability and safety of electricity supply, but also supports more efficient resource management, reduces operating costs and extends the service life of critical assets.

Optimising long-life assets is particularly important in the energy sector, where assets such as transformers, high-voltage lines, substations and turbines are designed to operate for decades. Through predictive maintenance strategies and continuous monitoring, companies can tailor interventions to the actual condition of each asset, avoiding premature replacement and maximising performance throughout the asset lifecycle. This approach supports investment prioritisation, improves long-term planning and ensures a more efficient, safe and sustainable operation of critical infrastructure.

The early detection of anomalies through predictive maintenance helps reduce unplanned interruptions, supporting continuity of electricity supply and a safer operating environment.

Impact on continuity of supply and safety

In energy infrastructure, continuity of electricity supply and safety depend largely on the condition of assets and the ability to detect anomalies at an early stage. Continuous monitoring and data analysis make it possible to identify degradation processes in critical equipment such as transformers, substations, lines and turbines before they develop into operational failures that could compromise system performance.

This approach also has a direct impact on the safety of facilities and personnel by enabling the identification of abnormal operating conditions that may create risk situations. Anticipating maintenance interventions reduces the need for reactive actions and emergency repairs, contributing to a more controlled and stable operating environment across electricity networks and generation systems.

What are the advantages and disadvantages of predictive maintenance?

Predictive maintenance helps ensure that equipment is only taken offline when data indicates a high risk of failure, reducing operating costs, minimising downtime and improving overall equipment performance. It also delivers significant benefits in asset management, as it not only anticipates failures but also enables more informed decisions regarding operation, maintenance, replacement and asset prioritisation. This translates into better investment planning, extended asset lifecycles, fewer unnecessary interventions, reduced impact from unplanned outages and support for safer and more efficient operations.

Among its disadvantages are the need for high-quality and readily available data, the initial investment required for sensors and digital infrastructure, integration between operational and analytical systems and the need for specialised personnel capable of interpreting the results. What’s more, the effectiveness of predictive maintenance depends heavily on equipment instrumentation: incomplete or insufficient data can limit the reliability of predictions.

What are the most widely used predictive maintenance techniques?

There are a number of techniques linked to predictive maintenance and we review some of them below:

Infrared thermography

Worn parts and components, including electronic circuits, often emit more heat than normal. By using infrared (IR) cameras, maintenance personnel are able to detect high temperatures (hot spots) on equipment.

Acoustic monitoring

Acoustic sensors enable maintenance personnel to detect gas, liquid or vacuum leaks in equipment. Friction and stresses in machines from worn or poorly lubricated bearings can also be detected.

Vibration analysis

It allows technicians to analyse the vibrations of a machine by means of sensors integrated into the equipment. A machine operating under optimal conditions has a specific vibration pattern, but when components wear out the vibration frequencies change.

Electrical monitoring and current analysis

This technique focuses on the continuous monitoring of electrical parameters such as current, voltage, power and power quality across equipment and systems. By analysing these variables, it is possible to detect electrical losses, overloads, phase imbalances and early-stage failures in motors, transformers, switchgear and power supply lines. Early identification of these anomalies makes it possible to anticipate degradation in electrical components and prevent failures that could lead to service interruptions or equipment damage.