Predictive maintenance: efficiency and complete control in your plant

In today's industry, every minute of production counts, so anticipating failures is more cost-effective than reacting to them.

Table of contents:

1. Introduction to predictive maintenance
2. What is predictive maintenance
3. How predictive maintenance works
4. Common predictive maintenance techniques
5. Benefits of predictive maintenance
6. Industry use cases
7. How to implement predictive maintenance in your company

Automation, the Internet of Things (IoT), and data analytics have transformed the way companies manage their assets. This is where industrial predictive maintenance comes into play, a methodology that allows you to detect anomalies before they become breakdowns, reducing costs and increasing equipment availability.

In this article, you will learn what predictive maintenance is, how it works, what technologies make it possible and why it has become a key pillar of Industry 4.0.

Introduction to predictive maintenance

Industrial companies have moved from reacting to failures to preventing them through data. In a highly competitive environment, it is not enough to maintain machinery: it is necessary to ensure its availability, reduce downtime and optimise resources.

Predictive maintenance has emerged in response to this need. Its aim is not just to repair, but to anticipate when a component will fail and act just before it does. Thanks to connected sensors, artificial intelligence (AI) and real-time analytics systems, industrial plants can predict failures with surgical precision.

In Industry 4.0, this capability translates into a direct competitive advantage: more productivity, fewer shutdowns and management based on real data, not intuition.

What is predictive maintenance?

Predictive maintenance is a strategy based on data analysis to predict the condition of equipment and determine when intervention is needed.

Unlike traditional methods, this approach uses real-time information (vibrations, temperature, power consumption, pressure...) to detect deviations from normal equipment behaviour.

Instead of following fixed schedules or waiting for a breakdown to occur, predictive maintenance makes it possible to act at the right time.

It is, in essence, the transition from reactive maintenance to an intelligent and proactive one, combining IoT, Big Data and machine learning algorithms.

Differences between preventive and corrective maintenance

• Corrective maintenance: acts after the failure. It is reactive and usually involves high costs due to unplanned shutdowns.
• Preventive maintenance: intervenes periodically, even if the equipment is still working well. It reduces risks, but can generate cost overruns due to unnecessary replacements.
• Predictive maintenance: analyses actual equipment data to anticipate failures and plan maintenance only when it is really necessary.

How predictive maintenance works

Predictive maintenance combines several technology layers: IoT sensors, connectivity, cloud storage, advanced analytics and MES or CMMS systems.

The goal is to collect, process and analyse large volumes of data to identify patterns that indicate impending failure.

Key technologies

1. IoT sensors: capture variables such as vibration, temperature, pressure, fluid level or power consumption. These devices transform machines into continuous data sources.
2. Big Data and Cloud Computing: They allow storing and processing millions of records generated in real time.
3. Machine Learning and Artificial Intelligence: Algorithms analyse data, learn from patterns and recognise deviations that anticipate failures.
4. MES and CMMS platforms: Integrate sensor information with operational planning, facilitating data-driven decisions.

Predictive modelling and AI

Predictive models are the heart of the system. Using AI, they compare the current behaviour of the equipment with its history and with expected standards. For example, an algorithm can detect that a machine is vibrating 5% more than normal, which can anticipate a bearing failure in the coming days.

This analytical intelligence allows maintenance to be planned without stopping production, improving overall system efficiency.

Process steps

1. Data Collection: continuous data collection using IoT sensors.
2. Data Mining: analysis of large volumes of historical data to identify trends.
3. Predictive modelling: training of algorithms that learn from patterns.
4. Prediction and action: the system issues automatic alerts and plans intervention at the optimal time.

Common predictive maintenance techniques

Techniques vary according to the type of asset or process. The following are the most commonly used in industrial environments:

Vibration analysis

Allows the identification of imbalances, misalignments or wear on rotating equipment. Sensors compare current frequencies with historical frequencies to detect anomalies. Application: pumps, electric motors, fans or turbines.

Infrared thermography

Measures the surface temperature of electrical or mechanical components. Hot spots indicate overheating or friction. Application: electrical panels, transformers, bearings or industrial brakes.

Acoustic monitoring

Uses ultrasonic sensors to detect leaks, friction or friction. Ideal for locating air or gas leaks before they become visible. Application: valves, compressors, piping and hydraulic systems.

Lubricant and ultrasonic analysis

Evaluates viscosity, presence of metallic particles or contaminants in industrial oils. Allows to know the internal wear without disassembling the equipment. Application: heavy machinery, diesel engines or gearboxes.

These techniques, combined in a single system, form the basis of an efficient industrial predictive maintenance strategy.

Benefits of predictive maintenance

Implementing predictive maintenance is not just a technical decision, but a business strategy based on measurable results.

1. Cost and downtime reduction

Preventing failures before they occur avoids emergency repairs and unexpected downtime. An unplanned shutdown can cost between €10,000 and €250,000 per hour, depending on the industry. With predictive maintenance, these costs are drastically reduced.

2. Improved availability and operational efficiency

Predictive maintenance optimises planning: technicians intervene only when necessary. This improves OEE (Overall Equipment Effectiveness) and makes it possible to maintain a more stable and profitable production.

3. Increased safety and extended asset lifetime

Detecting early failures prevents accidents and damage to critical components. In addition, assets are used to their optimum, without premature replacement.

4. Optimisation of spare parts inventory

By knowing when a component will fail, companies can plan purchases and reduce unnecessary stock. This frees up capital and improves maintenance logistics management.

According to the study "Predictive Maintenance 4.0 - Beyond the Hype" by PwC and Mainnovation with 268 industrial companies in Belgium, Germany and the Netherlands:

• Maintenance costs are reduced by 12%.
• Asset availability is improved by 9%.
• The service life of ageing equipment is extended by 20%.
• Safety, health, environmental and quality risks are reduced by 14%.

These figures demonstrate that predictive maintenance not only increases asset reliability but also optimises operational performance and strengthens industrial sustainability.

Industry use cases

Predictive maintenance has become a cross-cutting practice applicable to multiple industries.
Here are some sectors where it is already showing measurable results.

• Wind turbines and power grids: Energy companies use IoT sensors and predictive analytics to anticipate failures in wind turbines and transformers. This results in reduced downtime and optimised remote maintenance.

• Railroads and transportation: Railway systems apply predictive maintenance to monitor vibrations, brakes and energy consumption. This makes it possible to detect anomalies in axles or tracks before they cause incidents, increasing safety and punctuality.
• Manufacturing industry: In discrete and process manufacturing, predictive maintenance ensures the continuity of production lines and avoids bottlenecks. With solutions such as MES INEXION, it is possible to connect sensors, collect data and generate automatic alerts from a single panel.

Each of these cases demonstrates how predictive maintenance not only reduces failures but also increases industrial competitiveness.

How to implement predictive maintenance in your company

Adopting an industrial predictive maintenance strategy requires planning, technological integration and training. These are the key steps to do it successfully.

Step 1: Needs and critical assets assessment

The first step is to identify the most relevant assets for business continuity. Potential losses in the event of failure must be analysed, and those machines where the economic impact is the greatest must be prioritized.

Step 2: Integration of IoT and data systems

Installing IoT sensors on critical equipment and establishing a secure communication network is essential. These sensors must send data to a centralising platform (such as an MES or CMMS system).

Step 3: Choosing compatible MES/GMAO software

The software acts as the brain of the predictive system. Solutions such as MES INEXION allow:

• Monitor equipment in real time.
• Automatically detect anomalous patterns.
• Connect maintenance, production and quality on a single platform.

This integration reduces the gap between data and decision, driving overall plant efficiency.

Step 4: Metrics and KPIs to measure success

To evaluate the effectiveness of predictive maintenance, it pays to follow metrics such as:

• MTBF (Mean Time Between Failures)
• MTTR (Mean Time To Repair)
• Asset availability
• Cost per maintenance hour
• Return on Investment (ROI)

Predictive maintenance must deliver tangible results: fewer failures, higher availability and better cost control.

Industrial predictive maintenance is a competitive necessity. It allows moving from reactive maintenance to intelligent prediction, optimising resources, avoiding shutdowns and extending the useful life of assets. In a context where efficiency, traceability sustainability define industrial success, companies that integrate this strategy are leading the transition to the connected factory.

Discover INEXION and how it connects equipment, data and people on a single platform to anticipate failures and make decisions based on real information.

Schedule a meeting and discover how to improve the efficiency and availability of your plant.