For a long time, French-language literature has maintained an ambiguity between preventive maintenance and predictive maintenance.
In reality, according to the AFIM consensus and standard NF EN 13306, these two terms refer to exactly the same approach: “Conditional maintenance performed following a prediction obtained through repeated analysis or known characteristics and an assessment of the significant parameters of the asset’s deterioration.”
In other words, predictive maintenance consists of predicting a failure before it occurs, based on actual signals from the equipment.
From scheduled maintenance to data-driven maintenance
Systematic maintenance, also known as preventive maintenance, is based on fixed intervals (every 2,000 hours, every six months, etc.). It guarantees a certain level of reliability, but does not take into account the actual variability of usage conditions, environments, or processes. As a result, maintenance is sometimes performed too early (over-maintenance) and sometimes too late (unplanned breakdowns resulting in higher or lower costs).
Provisional maintenance, or predictive maintenance, takes the opposite approach: it observes the actual condition of assets. It uses sensor data (vibrations, temperature, pressure, intensity, acoustics, etc.), models the normal behavior of equipment, and then identifies small but significant deviations that indicate a problem.
This is the principle of Early Anomaly Detection: identifying the first weak signals before they result in a proven breakdown. Intervention then becomes “just in time”: neither too early nor too late, with maximum efficiency and risk control.
A key driver of Industry 5.0
Industry 4.0 focused on connectivity and automation: IoT, Big Data, AI, robotics.
Industry 5.0, on the other hand, puts people and sustainability back at the heart of decision-making.
In this context, predictive maintenance becomes a strategic lever:
- it secures operations
- it reduces the environmental footprint by avoiding unnecessary intervention
- it enhances human expertise through data-driven decision support tools.
What predictive maintenance brings to the table
1. Anticipate critical failures
Predictive algorithms detect the first signs of deterioration: a bearing that vibrates slightly more, a temperature that slowly drifts, pressure that deviates from the norm. This foresight allows interventions to be organized in advance: parts made available, skills mobilized, planned shutdowns.
2. Extend the service life of equipment
By intervening at the optimal moment, we avoid both sudden failure and premature maintenance.
The result: extended equipment and machine life and a better return on investment.
3. Optimize costs and availability
Fewer unplanned shutdowns, less lost production, less unnecessary maintenance: the value is direct and measurable.
4. Improve safety and reliability
Early warnings help prevent risky situations and ensure service continuity.
💡 Since the terms “preventive maintenance” and “predictive maintenance” are often used interchangeably, we clarify the distinction between them in a dedicated article.
Technologies at the heart of predictive maintenance
- Smart sensors and IIoT: continuous acquisition of critical data.
- Artificial intelligence and machine learning: anomaly detection, learning failure signatures.
- Physical, statistical, and hybrid modeling: combining physical laws and data.
- Digital twins: simulating real behavior to estimate degradation.
At Amiral Technologies, this approach translates into unsupervised models capable of learning the normal behavior of equipment with no failure history and automatically detecting deviations from time series.
Challenges and best practices
Towards sustainable and responsible maintenance
Even though predictive maintenance is an obvious technological choice, its implementation remains challenging:
- data quality: consistency, sampling frequency, contextualization;
- data science and engineering skills;
- integration into the maintenance workflow (CMMS/EAM) to transform alerts into actions;
- data security and sovereignty: key challenges for Industry 5.0.
Success depends above all on a pragmatic and gradual approach: start with a critical scope, measure the gains, and capitalize on field experience.
💡 To learn more about the transition to predictive maintenance, take a look at our dedicated article.
The era of predictive maintenance is not limited to technical performance. It is part of an ethical and sustainable approach:
- limiting waste and parts consumption
- enhancing operator safety
- reducing equipment energy consumption
- promoting knowledge transfer through explainable tools.
The combination of AI, engineering, and human expertise paves the way for more reliable, more efficient, and more human maintenance: a true pillar of Industry 5.0.
Conclusion
The confusion between preventive maintenance and predictive maintenance is now a thing of the past. Both terms refer to the same approach: anticipating equipment and machine failures using data.
This new generation of maintenance transforms the maintenance function into a strategic player in industrial performance. It is not just a technology, but a cultural change: acting before failure occurs, with discernment, based on real signals and explainable models.
