Predictive maintenance with IoT and EAM: anticipating failures in critical assets
In an increasingly data-driven industrial context, the ability to anticipate failures is extremely valuable at an operational level. When critical equipment fails unexpectedly, it affects production, costs, safety and reputation.
It is against this backdrop that predictive maintenance, supported by IoT (Internet of Things) and EAM (Enterprise Asset Management) systems, takes on a central role.
From reactive to predictive: a paradigm shift
For decades, maintenance has followed two main models: reactive (acting after a failure) and preventive (acting at set intervals). Both have clear limitations: either one reacts too late, or one intervenes unnecessarily.
Predictive maintenance introduces an approach based on real-time data. IoT sensors installed on assets continuously collect data such as vibration, temperature or pressure. This data is then analysed by algorithms that identify patterns and anomalies, anticipating potential failures before they occur.
In many cases, this capability is enhanced through the use of Digital Twins, as explored in a previous article, which virtually replicate assets and enable the simulation of behaviour and the prediction of failures with greater accuracy. This way, this predictive model enables maintenance to be transformed into a smart, continuous process that is aligned with the actual condition of the equipment.
The role of the IoT in early detection
The IoT is the key to making this transformation possible. By connecting physical assets to digital platforms, it becomes possible to monitor their behaviour in real time.
Thanks to IoT, the signals are analysed to trigger action
For example, subtle variations in an engine’s vibration can indicate progressive wear. Without sensors, this signal would go unnoticed until a failure occurred.
With IoT, data is captured, analysed and converted into an actionable alert.
According to Hexagon, now known as Octave in the industrial software sector, data-driven predictive maintenance solutions can reduce downtime for critical assets by between 5% and 15%, on average. This impact is particularly significant in industries where every hour of downtime results in significant losses.
EAM: from analysis to action
While the IoT provides data, EAM provides context and operationalisation.
Enterprise Asset Management systems integrate historical data, maintenance plans, work orders and operational data. When combined with predictive insights, they enable:
- Automatically create maintenance orders based on alerts
- Prioritise interventions according to the criticality of the asset
- Optimise resources and downtime
- Maintain a structured record for continuous improvement
Without this layer, the data remains isolated. With EAM, these become concrete decisions.
Tangible and measurable benefits
The adoption of predictive maintenance is not merely a technological trend.
Predictive maintenance reduces operational waste
According to Octave, this type of approach can also reduce operational waste, with scrap and rework reductions of up to 32% in certain industrial contexts.
They can also lead to significant reductions in maintenance costs and increases in asset availability, thereby enhancing the ROI of this type of strategy.
Challenges to consider
Despite the benefits, implementation is not without its challenges. Among the most common:
- Fragmented data: information scattered across multiple systems makes analysis difficult
- Data quality: poorly calibrated sensors or incomplete data compromise forecasts
- Technological integration: aligning IoT, analytics and EAM require a robust architecture
- Cultural change: teams accustomed to reactive models may resist change
Overcoming these obstacles is essential to ensure that predictive maintenance does not remain merely a theoretical concept.
Think ahead to stay competitive
In a scenario where global downtime results in losses of billions each year and can affect up to 20% of production capacity in some organisations, foresight and technological expertise are the solution.
The combination of IoT and EAM enables companies to move from a reactive approach to one based on prediction and optimisation. It is not just about preventing failures, but about creating operations that are more resilient, efficient and future proof.
In this regard, having partners such as Izertis, with expertise in technology integration and asset management, makes all the difference, from choosing the solution to defining the most appropriate strategy.